# Opero: full corpus

> Concatenated markdown of every content page on opero.pro. One file, sectioned by content type, suitable for ingestion by LLM crawlers that prefer fewer requests. The per-page mirrors at `<url>.md` remain available and are indexed in `/llms.txt`.

# About Opero

Source: https://www.opero.pro/about

Opero is an AI software and implementation partner that helps industrial and manufacturing teams find reliable answers and automate operational work.

- **Company name:** Opero · Opero Labs ApS
- **Type:** AI software and implementation partner for industry and manufacturing
- **Founded:** 2025
- **Founders:** Redouane Achouri and Mircea Gabriel Eftemie
- **Company register:** CVR 45698629, Denmark
- **Headquarters:** Frederiksholms Kanal 30, Copenhagen, Denmark
- **Locations:** Copenhagen, Denmark, and Stockholm, Sweden
- **Website:**  [www.opero.pro](https://www.opero.pro/)
- **Core offering:** Company knowledge, AI agents, and workflow automation connected to business systems
- **Pricing:** Starter: $29 per seat/month. Pro: $79 per seat/month. Enterprise: custom pricing. Usage charges and add-ons may apply. [Full pricing and allowances](/pricing)
- **Contract terms:** Monthly and annual billing options; enterprise terms depend on the agreement. [Plan details](/pricing)
- **Services:** Assessment, knowledge base setup, integrations, workflow implementation, training, and production support
- **Communication:** Email and assessment enquiries. [hi@opero.pro](mailto:hi@opero.pro)
- **Support response time:** We respond to support requests within 30 minutes in 95% of cases.
- **Notable clients:** Mitsubishi Logisnext, OP System, Zeppelin / Caterpillar DK, Hydraulico, Nize Equipment, Wicotec Kirkebjerg, BST Fire Protection, Nordic Electric, Koppers DK, Naturfrisk Group, Unox
- **Published customer stories:** Nize Equipment and Nordic Electric. [Read customer stories](/resources?type=case-study)
- **Hosting:** EU data centres; custom deployment options available. [Platform and trust](/trust)
- **Alternatives to compare:** ChatGPT, Microsoft Copilot, Dust, Glean, and ServiceNow. [Compare with Opero](/compare)
- **Social:**  [Opero on LinkedIn](https://www.linkedin.com/company/operopro)

# Industries

---

## Construction & Material Handling

Source: https://www.opero.pro/industry/machinery

**Heavy equipment, dealer networks, and uptime commitments**

Help technicians find the correct manual, service instruction, and spare part for the machine in front of them, wherever they are working.

## Customers in this industry

- HG Machines
- Logisnext
- OP System
- Zeppelin

## A stopped machine can stop the customer’s entire operation

When a forklift fails at a loading dock or a loader stops on a construction site, the customer needs the correct diagnosis, part, and next action quickly.

Technicians work away from a desk and support many brands, models, and years. The answer may be spread across a manual, a later service bulletin, a parts catalogue, and the experience of a senior colleague.

Opero gives the technician one place to ask by chat or voice. It searches information for the specific machine, shows the source, and can continue into a service case or parts request.

## Find information for the correct machine

Two machines with the same product name may use different components or repair procedures. A bulletin for an older model may also have been replaced.

Opero narrows the search using the brand, model, year, serial-number range, and document date. It avoids withdrawn instructions and links to the current source page.

If the information is incomplete or the next step is unsafe, Opero sends the case to a person instead of guessing.

## Support technicians across locations and languages

Dealer networks may cover several countries while official service documents remain in one language. Opero supports more than 40 languages in chat and voice.

A technician can ask in their preferred language and still open the original source. Voice also works beside the machine when typing is impractical.

## Move from a symptom to the next action

After finding the likely cause, Opero can check the parts catalogue, current stock, warranty, and customer contract. It can prepare a work order or parts request for review.

The technician does not repeat the same machine details across several systems, and the service office receives a structured case with useful context.

## Example: a forklift stops during a busy shift

A customer reports a warning code and gives the forklift’s vehicle number. Opero identifies the model, finds the current service bulletin, and guides the customer or technician through approved checks.

If a component needs replacement, Opero identifies the current part number, checks availability, and prepares the case. If the problem remains uncertain, it transfers the case to a senior technician with the conversation and sources attached.

## Keep dealer and customer information separate

Each person only sees the documents, prices, contracts, and customer records they may use. Read access is the default, and placing an order or changing a work order can require approval.

Every answer and action is recorded for later service or warranty review.

## Where Opero helps

- Technical support and fault diagnosis
- Technician onboarding and training
- Customer self-service under the dealer’s brand
- Parts identification and order preparation
- Service-call intake and work-order creation
- Recurring maintenance workflows

## Where to look next

- [Knowledge Agent](/product/knowledge) — search manuals, bulletins, cases, and training material.
- [Parts & Procurement](/product/parts) — identify a spare part and prepare an order.
- [AI Voice Agent](/product/voice) — answer routine service calls and transfer difficult cases.
- [Nize Equipment case study](/case-study/nize-equipment) — see measured service results.

---

## Industrial Production Machinery

Source: https://www.opero.pro/industry/production

**Presses, packaging lines, and production equipment**

Help production and service teams diagnose faults, find current instructions, and prepare the right service or parts response before downtime grows.

## Customers in this industry

- Hydraulico
- Nize Equipment

## One machine fault can hold up an entire production line

When a press, cutter, packaging line, or finishing machine stops, the cost grows with every hour. The answer may be hidden in a long manual, updated service bulletin, parts catalogue, or previous repair record.

Opero brings that information together. A technician can describe the symptom or fault code and receive an answer for the correct machine version, with the source visible beside it.

## Avoid instructions for another machine version

The same fault code can mean different things across product families and model years. Repair instructions can also change when a service bulletin replaces an older procedure.

Opero uses the model, serial number, production year, and document revision to search the applicable information. It points to the current source and avoids withdrawn instructions.

If the information does not support a safe answer, Opero sends the case to an experienced engineer instead of guessing.

## Help every shift use the same knowledge

Production equipment often runs around the clock, while experienced engineers are not available on every shift or site.

Operators and technicians can ask Opero by chat or voice in more than 40 languages. The official source remains linked even when the question and answer are translated.

## Continue from diagnosis to repair

Once the likely problem is identified, Opero can help find the current part number, check stock, and prepare a work order or purchase request. A person reviews actions involving cost, safety, or production commitments.

The next team receives the machine details, symptoms, checks completed, and sources already consulted.

## Example: a recurring packaging-line fault

An operator reports fault F-44 during the night shift. Opero identifies the line and searches its current manual and service bulletins.

It explains the approved checks and cites the page. Because the same fault has appeared twice recently, Opero flags the pattern for the maintenance manager. If a component is required, it prepares the part and work-order information for review.

The immediate fault is handled faster, and the recurring problem becomes visible before another stop.

## Keep production and engineering data controlled

Opero follows existing user and document permissions. Operators can receive safe guidance without seeing confidential engineering or commercial files.

Every question, source, and completed action is recorded for quality reviews, maintenance analysis, and later troubleshooting.

## Where Opero helps

- Fault diagnosis and service support
- Shift-to-shift knowledge sharing
- Technician and operator training
- Maintenance planning and recurring-fault detection
- Parts identification and order preparation
- Service reports and customer updates

## Where to look next

- [Knowledge Agent](/product/knowledge) — find cited answers across manuals, bulletins, videos, and cases.
- [AI Workflows](/product/workflows) — connect fault reports, maintenance plans, approvals, and systems.
- [Operations and plant managers](/use-case/ops) — make recurring faults and incomplete handovers easier to see.

---

## Building Technical Services (MEP)

Source: https://www.opero.pro/industry/mep

**Electrical, heating, cooling, fire, and plumbing services**

Help project and service teams find the correct drawing, handover record, code reference, and commissioning detail for the building they are working on.

## Customers in this industry

- Wicotec Kirkebjerg
- BST Fire Protection
- Condair
- Nordic Electric

## The answer exists, but it is filed under the project

A service engineer arrives at a building with a fault. The useful information may be in the handover pack, commissioning report, subcontractor email, drawing revision, or equipment schedule.

Finding it can take longer than the repair, especially when the company runs thousands of projects and several buildings use similar equipment.

Opero lets the engineer ask in everyday language and searches within the correct project. It returns the answer with a link to the drawing, report, or document behind it.

## Keep similar projects from being mixed together

Two buildings may use the same equipment model but have different settings, contractors, commissioning dates, and local requirements. Searching every company file at once can surface a correct answer for the wrong site.

Opero narrows the search using the project, building, system, equipment, location, and document revision. The engineer sees the source and can confirm that it belongs to the building in front of them.

## Preserve knowledge after handover

Important decisions are often buried in a project folder once construction ends. When the original project manager or subcontractor is unavailable, the service team has to reconstruct what happened.

Opero makes drawings, test records, punch lists, commissioning notes, and approved correspondence searchable together. New service employees can find the history without relying on the memory of the person who delivered the project.

## Turn a request into a complete service case

Opero can identify the building and equipment, collect relevant documents, prepare a service case, and send it to the correct team.

If a specialist is needed, the case arrives with the customer’s description, project context, and sources already checked. The specialist continues the work instead of starting the search again.

## Example: an alarm after commissioning

Six months after handover, a building reports an alarm on an air-handling unit. The engineer asks Opero for the approved settings.

Opero searches the project and finds the final commissioning record rather than the manufacturer’s defaults or a similar unit from another building. It shows the source, setting, and drawing revision.

## Keep access aligned with projects and roles

Employees, subcontractors, and customers may need different information. Opero follows your access rules so each person only sees permitted projects and documents.

Every answer and action is recorded for quality reviews, contractual questions, and later investigation.

## Where Opero helps

- Project handover and commissioning support
- Building service and fault diagnosis
- Drawing and document search
- Technician onboarding
- Service-case preparation and routing
- Inspections, reports, and maintenance workflows

## Where to look next

- [Knowledge Agent](/product/knowledge) — search project records with visible sources.
- [AI Workflows](/product/workflows) — automate service intake, inspections, reports, and approvals.
- [AI for MEP contractors](/resources/ai-for-mep-contractors) — choose a practical first use case.

---

## Food & Beverage Manufacturing

Source: https://www.opero.pro/industry/food

**Commercial kitchens, beverage lines, and food production**

Give operators approved answers for equipment, cleaning, and production procedures at any site or shift, in the language they understand best.

## Customers in this industry

- Naturfrisk
- Unox

## A night shift cannot wait for the morning expert

Food and beverage equipment runs across sites, shifts, and languages. When an oven, filling line, refrigeration system, or packaging machine reports a problem, the operator needs the correct procedure immediately.

The answer may be in an equipment manual, cleaning instruction, production procedure, or quality document that the operator has never seen. Calling an engineer at another site delays production and pulls an expert away from other work.

Opero gives operators one place to ask by chat or voice. It finds the approved information for the correct equipment and process, answers in plain language, and shows the source behind the answer.

## Give every shift access to the same instructions

Opero can search equipment manuals, standard operating procedures, sanitation instructions, training videos, maintenance records, and approved quality documents together.

It supports more than 40 languages. An operator can ask in their preferred language even when the official procedure is written in another one. The answer still links to the original source for verification.

This helps teams use consistent instructions across sites without translating and maintaining a separate copy of every file.

## Treat food-safety questions differently from routine faults

Some questions are operational; others may affect food safety, quality, or regulatory compliance. Opero can recognise when a question refers to a controlled step or approved limit and route it according to your rules.

It can provide the approved procedure and source, but it does not invent a decision when the evidence is missing. Questions involving a possible safety or quality deviation can be sent to the responsible person before production continues.

## Example: a temperature warning during the night shift

An operator sees a temperature warning during a production run. They ask Opero by voice and identify the line and product.

Opero finds the current procedure for that line, explains the required checks, and cites the exact source. Because the warning may affect a controlled production step, it also notifies the shift supervisor and records the event.

The operator receives immediate guidance, while the decision that requires quality responsibility remains with the right person.

## Make recurring problems visible

Opero records questions and the sources used to answer them. Repeated faults, unanswered questions, and frequently used procedures become visible to maintenance, training, and quality teams.

This can show where equipment needs attention, where an instruction is unclear, or where the next training update should focus.

## Keep sites, products, and roles separate

Employees only receive information relevant to their site, role, equipment, and product access. Sensitive quality, formulation, or commercial documents remain restricted.

Every answer and completed action is recorded for authorised review, supporting later investigation and continuous improvement.

## Where Opero helps

- Operator and maintenance support across shifts
- Equipment troubleshooting
- Cleaning and changeover procedures
- Training-video and document search
- Escalation of quality and safety questions
- Shift handovers and recurring-fault reporting

## Where to look next

- [Knowledge Agent](/product/knowledge) — search procedures, manuals, spreadsheets, and training videos together.
- [AI Voice Agent](/product/voice) — let operators ask for help without leaving the line.
- [Operations and plant managers](/use-case/ops) — structure handovers and identify recurring operational problems.

# Products

---

## Knowledge Agent

Source: https://www.opero.pro/product/knowledge

*An AI knowledge base that answers your technicians from your own manuals.*

Technicians ask by chat or voice and get a cited answer from manuals, service bulletins, CMMS history and past cases. Less downtime, fewer calls to the expert.

## Key capabilities

- Ask questions in everyday language
- See the document, page, or video moment behind each answer
- Search PDFs, spreadsheets, videos, and past cases together
- Respect existing document permissions
- Use more than 40 languages in chat and voice
- Run in the cloud or your own environment

## Troubleshooting answers are buried in manuals, bulletins and old cases

A technician may know the machine and symptom but not which manual contains the answer. The latest instruction may be in a service bulletin, while the useful fix lives in an old support case or training video.

Searching takes time, and asking a senior colleague interrupts two people. Using an older document or a manual for the wrong model can also lead to the wrong repair.

Opero lets people ask in everyday language. It searches the company information they may use, returns a clear answer, and links to the source so they can check it before acting.

## One knowledge base for manuals, drawings, videos and work orders

Opero can search manuals, drawings, service bulletins, parts catalogues, previous cases, approved emails, spreadsheets, and training videos. A document answer links to the relevant file and page; a video answer links to the exact moment where the information appears.

It can also group files automatically by their subject, so people do not have to know which folder somebody chose. Learn more about [automatic document folders](/resources/2026-08-31-document-folders).

## Get the answer for the machine in front of you

The same fault code can mean different things across products and model years. Opero uses details such as product line, model, serial number, document type, and publication date to narrow the search.

For example, “E-12” may mean low oil pressure on an older diesel machine and a battery warning on a newer electric model. Opero searches the applicable documents instead of guessing which meaning is correct.

If the available information is not enough to answer safely, Opero says so and can send the question to a person.

## Avoid withdrawn instructions

Service information changes. Opero records which documents replace older versions and directs the user to the current instruction.

The source remains visible beside the answer, including its revision and surrounding page. A technician can check warnings, specifications, and context before continuing.

## Ask in one language and search documents in another

Opero supports more than 40 languages in chat and voice. A technician can ask in Polish when the relevant manual is in German. Opero searches the German source, answers in Polish, and keeps the citation linked to the original page.

## Keep confidential information within its audience

Opero follows the document and user permissions your company already manages. A sales employee cannot receive a restricted engineering drawing, and a technician cannot see confidential pricing simply because both files are searchable.

Every question, source, answer, and completed action is recorded for authorised review.

## Proven with Nize Equipment

After Nize Equipment introduced the Knowledge Agent, escalations to senior support specialists fell by 60% within 90 days. Technician onboarding became 4.1 times faster, and 92% of the field team used it each week.

Read the [Nize Equipment case study](/case-study/nize-equipment).

## Use it where your team already works

People can use the Knowledge Agent through chat, voice, the customer portal, or a connected business system. It can start an approved next step such as opening a work order, checking a part, or updating a case.

Read access is the default. Permission to change information is granted separately for each system and action. See [Integrations](/product/integrations).

## Where to look next

- [Construction and material handling](/industry/machinery) — help technicians across many models, locations, and languages.
- [Service managers](/use-case/service) — reduce avoidable escalations and find missing service knowledge.
- [Operational trust](/resources/trust) — understand sources, permissions, and review records.

---

## AI Voice Agent

Source: https://www.opero.pro/product/voice

*Answer routine service calls with an AI voice agent, 24 hours a day.*

Customers and technicians ask by phone or browser. The voice agent answers from approved knowledge, updates work orders, or transfers the call with context.

## Key capabilities

- Answer incoming calls at any hour
- Make scheduled customer calls automatically
- Use the same approved knowledge as chat
- Speak more than 40 languages
- Transfer difficult calls with the conversation attached

## Routine calls take experts away from harder work

Service teams receive many calls that follow the same pattern. A customer reports a fault code, asks whether a part has arrived, or wants to book a technician. Someone answers, looks through several systems, explains the result, and records the call.

Those calls matter, but they do not always need a specialist from beginning to end.

The Opero Voice Agent answers using your approved company knowledge. It can look up the relevant customer or machine, explain the next step, open a service case, send a message, or transfer the caller to a person when judgement is needed.

## Help callers from the first question to the next action

When a customer calls, Opero can ask for a serial number, model, order number, or other detail needed to find the correct information.

It then checks your manuals, service bulletins, customer records, and connected systems. The answer comes from those sources, not from a general internet search.

If the call requires more help, Opero transfers it with the transcript, sources, and details already collected. The caller does not have to repeat the problem, and the employee taking over can see what has already been checked.

## Four ways to use voice

**Answer incoming calls.** Handle common service questions, order updates, booking requests, and basic troubleshooting at any hour.

**Make scheduled calls.** Tell a customer when a part arrives, remind them about an appointment, or collect feedback after a service visit.

**Help technicians while they work.** A technician can ask a question without stopping to type or remove their gloves. Opero answers from the same approved documents used by the office team.

**Talk in the browser.** Employees can press a button in Opero and speak from a laptop or phone without making a telephone call.

## Speak to customers in their preferred language

Opero supports more than 40 languages and understands regional accents. A caller can also change languages during the conversation without starting again.

Each team can choose its supported languages and preferred voice. When a person takes over, the transcript shows what was said regardless of the language used on the call.

## Example: a stopped machine at a loading dock

A customer calls because a forklift has stopped during a busy shift. They read the vehicle number from the machine plate.

Opero identifies the exact model and finds the current service instructions. It guides the customer through a short, safe check and identifies a likely failed sensor.

Opero then opens a work order with the relevant part attached, schedules a technician, and sends the customer a confirmation message in their preferred language. If the check is inconclusive or unsafe to continue, the call goes directly to a technician with the full context.

## Decide where a person must take over

You control:

- Which questions Opero may answer
- Which languages and phone lines it uses
- When it answers automatically and when calls go directly to the team
- Which actions it may complete, such as opening a work order or sending a text message
- Which actions require approval, such as placing an expensive parts order

Every call has a transcript. Opero also records which sources it used and which actions it completed, so your team can review what happened later.

## Connect it to your existing phone and business systems

Opero works with Twilio, Vonage, or your existing telephone provider. It can create cases and work orders in systems such as ServiceNow or Salesforce, and check parts or customer information in systems such as SAP, Dynamics, or IFS.

See all available connections at [Integrations](/product/integrations).

## Where to look next

- [Voice in the workshop](/resources/voicebot-workshop) — a closer look at hands-free help for technicians.
- [Customer service use case](/use-case/cs) — how to choose which calls to handle first.
- [Knowledge Agent](/product/knowledge) — how Opero finds answers in approved company documents.

---

## AI Workflows

Source: https://www.opero.pro/product/workflows

*Turn repeated maintenance and service work into a workflow that runs the same way every time.*

Connect the steps, systems, AI decisions and human approvals behind service intake, warranty checks, preventive maintenance planning and order preparation.

## Key capabilities

- Describe the problem and let AI draft the workflow
- Connect more than 100 ready-made actions
- Keep human approval where judgement matters
- See every step while a workflow runs
- Record actions, changes, and approvals

## Repeated work becomes slow when it crosses several systems

Many processes are difficult because someone must read a request, find information in several systems, decide what happens next, copy details into another tool, and ask the right person for approval.

Opero turns those steps into a workflow. Routine work happens automatically, while decisions involving risk, money, customers, or uncertainty can still wait for a person.

## Describe the problem, not the technical implementation

Tell Opero the outcome you need in plain language. For example:

> When a job is marked complete, collect the expenses, check that the required information is present, and prepare an invoice for approval.

Opero drafts the steps and connections on a visual canvas. Your team checks the logic, changes any rule, and decides where approval is required before activating it.

You can also build directly with more than 100 ready-made actions. See the console in [Build a workflow yourself, or ask AI to start it](/resources/2026-08-31-workflow-builder).

## Connect the full process

A workflow can start from an email, schedule, form, phone call, or change in another system. It can read documents and records, turn unstructured information into useful fields, check business rules, create or update records, request approval, and notify the next team.

This improves a real process from beginning to end instead of automating one isolated click.

## Example: prepare an invoice when a job is complete

A field-service job is marked complete in Microsoft Business Central. Opero checks that the work description, time, materials, and expenses are present.

If something is missing, it asks the responsible person. When the record is complete, Opero prepares the invoice lines and creates a draft in e-conomic. A finance employee reviews it before it is sent.

The workflow removes chasing and copying but leaves the commercial commitment with the team.

## Example: plan preventive maintenance from CMMS data

A monthly maintenance window opens for equipment at three sites. Opero checks the asset register, service intervals, locations, technician skills, and required parts.

It prepares a schedule, parts list, and technician assignments. The coordinator resolves conflicts before appointments are confirmed instead of building the entire plan by hand.

## Put approval where it is needed

You choose which actions Opero may complete and which must pause for a named person. A workflow may prepare a purchase order but require approval above a spending limit, or draft a customer reply but wait for a manager when safety or warranty is involved.

Opero does not allow workflow steps that delete records. Read access is the default, and permission to change information is granted for each system and action.

## See what happens while it runs

Each run shows the completed, current, and next steps. If something fails, Opero explains the problem in plain language and can notify the responsible team.

The history records information used, actions completed, changes made, and people who approved or rejected a step.

## Build it yourself or with us

Your team can create workflows on the canvas. For an important first process, we can map and build it with you: what starts it, which information is checked, who decides, where delays occur, and which system holds the final record.

The workflow is tested with real examples before activation.

## Use your existing systems

Opero connects to SAP, IFS, ServiceNow, HubSpot, Microsoft Dynamics, IBM Maximo, Outlook, Slack, and other systems. See [Integrations](/product/integrations).

## Where to look next

- [Workflow builder guide](/resources/2026-08-31-workflow-builder) — see the canvas and example workflow.
- [Operations and plant managers](/use-case/ops) — structure handovers and recurring work.
- [RFP / RFQ Automation](/product/rfp) — coordinate documents, specialist answers, and approval.

---

## Integrations

Source: https://www.opero.pro/product/integrations

*Connect Opero to your CMMS, ERP, CRM and document systems.*

Connect Opero to your CMMS, ERP, CRM and document systems, so people and AI use current work orders, parts and documents without copying anything by hand.

## Key capabilities

- Keep the systems your teams already know
- Read current data from the original source
- Choose exactly what Opero may update
- Connect cloud and on-premise systems
- 240+ ready-made connectors

## Your information is spread across different systems

Customer details may live in a CRM. Orders and prices may live in an ERP. Service cases may be somewhere else, while manuals and drawings sit in SharePoint.

That separation creates work. People copy information between screens, ask colleagues to look things up, and make decisions using records that may already be out of date.

Opero connects those systems. It finds information where it already lives and can pass it to the person, agent, or workflow that needs it. You keep the software your teams already use and avoid moving everything into another platform.

## Use current information without copying it

Suppose a service request includes a machine serial number. Opero can use that number to find the customer in the CRM, check warranty coverage in the ERP, open the correct manual in SharePoint, and create a service case.

The technician receives one useful result instead of opening four systems and copying the same details between them.

The same approach works for sales, purchasing, finance, and operations. Opero can read from one system, apply your business rules, and update another when the process requires it.

## You decide what Opero is allowed to change

Opero starts with read-only access. It can look up information but cannot change a record.

You grant additional access for specific actions. For example, Opero may be allowed to create a service case but not delete one, or prepare a purchase order but wait for a person to approve it.

Every change is recorded with the action, time, system, and person or workflow that requested it. This gives your operations and IT teams a clear record of what happened.

## CMMS, ERP, CRM and document systems we already connect to

Opero has more than 240 ready-made connectors. Common examples include:

**Finance, inventory, and operations:** SAP S/4HANA, SAP ECC, IFS, Microsoft Dynamics 365, Infor M3, and e-conomic.

**Customer and sales systems:** Salesforce, HubSpot, Pipedrive, Attio, and Zoho CRM.

**Service and maintenance systems:** ServiceNow, IBM Maximo, Salesforce Field Service, and Zoho Desk.

**Documents and collaboration:** SharePoint, OneDrive, Microsoft 365, Teams, Confluence, Slack, and compatible file stores.

**Calls and messages:** Twilio, Vonage, email, and SMS.

**Login and user access:** Microsoft Entra ID, Okta, and Google Workspace. Opero follows the user groups and document permissions you already manage there.

## Cloud, on-premise, and older systems

Not every important system is in the cloud or has a modern interface. Opero can connect to software running inside your company network as well as cloud services.

For systems that cannot be reached directly, we install a secure connection inside your environment. Your data stays within the boundary your IT team has approved.

## If your system is not on the list

We can build a connector for your system and the way your company stores data. A typical connector takes two to four weeks, depending on the system and the quality of its API—the interface software uses to exchange information.

We define the information Opero needs to read, the actions it may take, and the permissions for each action before the connector goes live.

## Where to look next

- [AI Workflows](/product/workflows) — connect several systems into one repeatable process.
- [Knowledge Agent](/product/knowledge) — find answers across company documents while preserving access permissions.
- [Operations use case](/use-case/ops) — see how operations teams choose which process to connect first.

---

## RFP / RFQ Automation

Source: https://www.opero.pro/product/rfp

*Respond to more RFPs and RFQs without rushing the important answers.*

Draft an RFP or RFQ response from approved company documents and past bids, then give your team more time to review pricing, scope and commitments.

## Key capabilities

- Find reusable answers from past bids
- Build the requirements checklist automatically
- Draft directly in Word and Excel
- Flag pricing and contract questions for review
- Keep a person responsible for the final response

## Good opportunities are easy to miss when every bid takes weeks

A request for proposal (RFP) or request for quotation (RFQ) can contain hundreds of questions. The answers often exist somewhere in previous bids, product documents, certificates, price lists, or legal files—but someone still has to find them, check them, and copy them into the customer’s template.

That leaves the bid team with less time for the decisions that matter: what to offer, what it will cost, what can be delivered, and which terms the company can accept. When the team is already busy, a suitable opportunity may be declined simply because there is not enough time to prepare a response.

Opero handles the first round of document work. It finds each requirement, drafts answers from sources your company has approved, and shows where every answer came from. Your bid team reviews the draft and remains responsible for every price, promise, and final submission.

## Start with the customer’s original document

Upload the RFP or RFQ as a PDF, Word document, or Excel workbook. Opero identifies the questions and requirements, including statements containing words such as “must,” “shall,” or “required.”

It turns them into a clear checklist with:

- The original requirement
- Its location in the customer’s document
- A suggested answer
- The company source used to prepare that answer
- A warning when information is missing or needs specialist review

The bid manager can work through the list without repeatedly searching the full document or asking several departments for the same standard information.

## Reuse good work without copying old mistakes

Opero can search previous bids, product specifications, policies, certificates, case studies, and approved commercial material. You decide which sources it may use.

For example, you can include successful bids from the last three years while excluding expired certificates, discontinued products, or answers written for a different market. The draft follows your company’s terminology and writing style because it is based on your own material.

Every suggested answer links back to its source. If Opero cannot find enough evidence, it says so instead of inventing an answer.

## Give specialists only the questions that need them

Product, legal, security, finance, and delivery teams should not have to review every standard paragraph. Opero can send them the smaller set of questions that require their judgement.

A technical specialist may receive a product-performance question. Legal may receive a contract exception. Finance may receive a pricing or payment-term question. Their answer returns to the correct place in the bid, so the bid manager can see what is complete and what is still waiting.

## Example: a large equipment bid arrives at noon

A customer sends a 200-page RFP. The bid manager uploads it and Opero builds the requirements checklist.

By the end of the day, the team has a structured first draft. Standard company and product answers are filled from approved sources. Questions about delivery dates, local regulations, pricing, and contract terms are clearly marked for the right people.

The next day can be spent improving the offer and checking risk instead of copying paragraphs between files. If the opportunity is a good fit, the team can respond even when several other bids are already in progress.

## Keep pricing and commitments under human control

Opero does not submit the bid or make a commercial commitment on your behalf.

You choose:

- Which previous bids and documents may be used
- Whether Opero drafts technical, commercial, or both types of answers
- Which products, regions, and customers each source applies to
- Which pricing, margin, delivery, or contract questions must be flagged
- Who must review each section before the response is complete

The review history records what Opero suggested, what a person changed, who approved it, and when. This gives the bid manager a clear record of how the final response was prepared.

## Work in the files your customers require

Opero reads and prepares content for Word and Excel, so the team can return the response in the customer’s required format. It can use approved material stored in SharePoint, OneDrive, and other connected document systems.

See the available systems at [Integrations](/product/integrations).

## Where to look next

- [RFP automation playbook](/resources/rfp-playbook) — how to prepare source material and run the first bid with human review.
- [AI Workflows](/product/workflows) — route questions, approvals, and completed answers between the people responsible.
- [Sales use case](/use-case/sales) — see how faster first drafts can help a team respond to more suitable opportunities.

---

## Customer Support Portal

Source: https://www.opero.pro/product/portal

*Help customers solve routine machine problems before they call support.*

A self-service support portal under your brand. Customers find cited troubleshooting answers, open cases and order spare parts from information you approve.

## Key capabilities

- Your domain, logo, colours, and wording
- Answers from approved product and service documents
- Different access for each customer
- Works well on phones and workshop screens
- See which questions still need human support

## Customers need help when your team is busy

A customer with a stopped machine may only need a fault-code explanation, the correct manual, or a replacement part number. But if that information is difficult to find, the customer opens a ticket or calls a technician.

Your support team then spends time answering questions that the company has already documented.

Opero gives customers a self-service support site under your own brand. They ask a question in their own words and receive an answer from the documents you have approved. When the question needs a person, the portal can open a support case with the conversation and relevant product details already attached.

## It looks like part of your company

The portal uses your domain, logo, colours, and wording. Customers see your company in the browser and throughout the experience.

Behind the page, Opero searches the same manuals, service bulletins, and product information available to your internal support team. Each answer links back to its source so the customer can check it.

## Customers only see what they are allowed to see

Not every customer should have access to the same information. A customer with a service contract may see detailed diagnostics and service history. A warranty customer may see troubleshooting and parts ordering but not pricing.

The portal uses the customer accounts and access levels you already maintain. Customers sign in with their existing account, and Opero applies the correct permissions to every answer and action.

Each customer company's information remains separate. One customer's questions, documents, and history are not used when answering another customer.

## Example: a fault code at the start of a shift

A workshop manager sees fault code E-12 on a compact excavator at 06:45. Instead of waiting for the dealer support line to open, they scan the QR code on the machine.

The support portal opens on their phone and identifies the correct machine model. They enter the fault code. Opero finds the current service bulletin and returns:

- What the code means
- The checks to perform
- The relevant wiring diagram
- The correct part when a replacement is needed

If the problem cannot be solved safely through self-service, the portal opens a support case and includes the machine, fault code, conversation, and sources already checked. The technician starts with useful context instead of asking the customer to repeat everything.

## Let customers complete simple next steps

You choose what customers may do in the portal. Depending on their access, they can:

- Ask questions about products and service procedures
- Open a support case
- Register a warranty issue
- Check whether a part fits their machine
- Request or place a parts order

Actions involving cost, warranty approval, or safety can be sent to a person for confirmation.

## Learn where support information is missing

The dashboard shows which subjects customers ask about, which questions Opero cannot answer, and which documents are used most often. It does not expose customer conversations to other customers.

Repeated unanswered questions show where a manual, support article, or training resource may need improvement. This helps the support team fix the underlying information gap, not only close the next ticket.

## Proven with Nize Equipment

Nize Equipment first gave its internal team the Knowledge Agent. Within 90 days, escalations to senior support specialists dropped by 60%. The next step was giving customers access to the same approved knowledge before they opened a ticket.

Read the [Nize Equipment case study](/case-study/nize-equipment).

## What it connects to

The portal can use your existing customer login, send unresolved questions to your service-management system, and check parts, warranty, and order information in your ERP.

See the full list at [Integrations](/product/integrations).

## Where to look next

- [Knowledge Agent](/product/knowledge) — how Opero finds and cites answers from company documents.
- [Construction and material handling](/industry/machinery) — support patterns for dealers and equipment companies.
- [Customer service use case](/use-case/cs) — where AI can remove repetitive work from the support queue.

---

## AI Email Inbox

Source: https://www.opero.pro/product/inbox

*Handle service requests and sales email without making the inbox a full-time job.*

Triage team inboxes, filter spam, qualify leads, route service requests and prepare grounded replies from your documents, previous emails and connected systems.

## Key capabilities

- A real address on your own domain
- Support, sales, supplier, and internal-request triage
- Replies grounded in company knowledge and systems
- Human review or automatic sending by team
- Save useful attachments and threads to the knowledge base

## One inbox contains several different jobs

A shared inbox mixes support requests, sales enquiries, supplier updates, internal questions, mailing lists, and spam. Before anyone can solve a problem, someone has to read each message, work out what it is, find the right information, and decide who should handle it.

Opero gives the team an AI email inbox on your own domain. It reads the complete thread, identifies what the sender needs, and moves the message to the right next step.

## What happens when a message arrives

Opero can:

- Ignore spam and automatic replies.
- Separate support requests, sales leads, supplier messages, and internal work.
- Qualify a lead using the information in the message and your CRM.
- Find the relevant customer, order, machine, contract, or case in a connected system.
- Route the message to the correct person or workflow.
- Prepare a reply using approved company knowledge.

The inbox can use your documents, useful previous conversations, CRM, ERP, and other connected systems. The answer is based on the information your company already trusts, rather than a generic response from the public internet.

## Choose what Opero may do on its own

Different teams need different levels of control. A support team may allow Opero to send a routine answer automatically when the source is clear. A sales team may require a person to review every first reply. A finance inbox may allow classification and routing but no automatic sending.

You set those rules per team. Opero can:

1. Save a draft for a person to review.
2. Route the message with a suggested next action.
3. Send automatically when the evidence and business rules are clear.

Replies come from the team inbox and keep the existing recipients in the conversation.

## Turn useful email into company knowledge

Important information often remains inside one mailbox. Opero makes it reusable:

**Save an attachment.** Add a drawing, specification, label photo, service report, or signed contract to the knowledge base.

**Save a conversation.** Turn a useful email thread into a document so the next person can find the answer without searching somebody else's inbox.

This means the inbox gets better at helping the team without relying on one person to remember every previous case.

## Common uses

**Customer support.** Identify the product and issue, check the manuals and previous cases, then draft an answer or open a service case.

**Sales qualification.** Extract the company, product interest, location, timing, and other useful details, check the CRM, and route the lead to the right salesperson.

**Supplier updates.** Match a delivery notice or order change to the correct purchase order, update the relevant system, and notify whoever is affected.

**Internal requests.** Turn a request sent to finance, operations, or HR into a structured task with the relevant details attached.

## What it connects to

Customer and lead details can come from your CRM. Orders, invoices, and product information can come from your ERP. Documents can come from SharePoint or your Opero knowledge base. The inbox can also start an [AI workflow](/product/workflows) when the request needs several steps or an approval.

See the full connector list at [Integrations](/product/integrations).

## Where to look next

- [Knowledge Agent](/product/knowledge) — how Opero finds grounded answers in company documents.
- [AI Workflows](/product/workflows) — how an incoming message can start a larger business process.
- [Summer 2026 product update](/resources/2026-08-31-product-update) — the release that introduced the AI email inbox.

---

## Parts & Procurement

Source: https://www.opero.pro/product/parts

*Find and order the right spare part without searching several systems.*

Help technicians identify the correct spare part, check stock, price and warranty, and prepare a purchase order for approval from one conversation.

## Key capabilities

- Identify parts from symptoms, photos, and machine details
- Check current part numbers and replacements
- Confirm stock, price, warranty, and contract coverage
- Prepare purchase orders for human approval
- Use your existing catalogues and business systems

## Finding the right spare part often takes longer than expected

A technician knows what the machine is doing, but the parts catalogue may use a different name. The same component may also have several versions depending on the model, year, region, or serial number. An older part number may have been replaced by a newer one.

To place an order, someone may need to open the service manual, parts catalogue, stock system, customer contract, and warranty record. One wrong selection can delay the repair and create another return, shipment, and invoice correction.

Opero brings those checks into one guided conversation. It helps identify the likely part, confirms which version fits the machine, checks the information needed for the order, and prepares it for a person to approve.

## Start with the information the technician already has

The technician can describe the symptom, enter a fault code, provide the machine model or serial number, or upload a photo. Opero uses that information with your service documents and parts catalogues to narrow the search.

It can show:

- The likely failed component
- The current part number
- Any replacement for an older part number
- The machines and serial-number range it fits
- The document or catalogue entry supporting the result

If the information is not enough to identify one part safely, Opero asks a follow-up question or sends the case to a parts specialist.

## Check availability before promising a delivery date

After identifying the part, Opero can check your stock system and approved suppliers. It shows whether the part is available locally, held at another warehouse, or needs to be ordered from a supplier.

The result comes from the current stock record. This helps the team give the customer a realistic answer instead of promising a part that is not available at the expected location.

## Apply the right price, warranty, and contract

The same part may be handled differently for each customer. It may be covered by a warranty, included in a service agreement, sold at a negotiated price, or require approval above a spending limit.

Opero checks the customer and machine records before preparing the order. It can attach the relevant warranty or contract reference and flag anything that needs a manager’s decision.

## Example: a forklift slowly loses hydraulic pressure

A technician reports that a forklift’s lift cylinder slowly drops while carrying a load. They provide the model and vehicle number.

Opero checks the service information and identifies the seal kit fitted to that machine version. It finds the current kit number, confirms the included items, checks local stock, and verifies that the customer’s service agreement covers the parts.

Opero prepares the order with the part, quantity, stock location, and contract reference already filled in. The parts manager checks it, makes any necessary change, and approves it without repeating the full search.

## Keep a person in control of every order

By default, Opero prepares an order but does not send it. A named person reviews and approves it first.

You choose:

- Which catalogues each team or customer may use
- Which warehouses and suppliers should be checked
- When a warranty or service contract applies
- Which spending limits require additional approval
- Whether a low-risk order may be sent automatically
- Who can approve, change, or cancel an order

Every draft, change, and approval is recorded with the person and time, giving procurement teams a clear history of the decision.

## Use the systems you already have

Opero can connect to ERP systems such as SAP, IFS, and Microsoft Dynamics for stock, customer, price, and order information. It can also use manufacturer catalogues, supplier catalogues, and warehouse systems.

This means the part number and availability come from the systems your company already maintains rather than from a separate copy that may become outdated.

See the available systems at [Integrations](/product/integrations).

## Where to look next

- [Knowledge Agent](/product/knowledge) — find the service information that connects a symptom to the likely failed component.
- [Construction and material handling](/industry/machinery) — see how equipment dealers support technicians and customers across many machine models.
- [Procurement use case](/use-case/procurement) — connect order preparation, spending limits, and approval rules to your existing process.

# Use cases

---

## C-suite

Source: https://www.opero.pro/use-case/csuite

**Business outcomes, controlled risk, and measurable adoption**

Connect each AI initiative to service, sales, operational, or procurement outcomes that leaders can review by business unit.

## Leaders need to know what changed, not how much AI was used

An AI pilot can produce impressive demonstrations without improving a business result. Executives still need to know whether service became faster, more bids were answered, fewer parts were returned, or employees recovered time for higher-value work.

They also need evidence that permissions, approvals, and customer commitments remain controlled.

Opero connects each use case to a defined operational outcome and records the actions behind it. Leaders can review results by team or business unit without treating “AI usage” as success on its own.

## Begin with one costly business problem

A useful first project has a clear owner, an existing process, and an outcome the company already understands.

Examples include reducing avoidable service escalations, shortening the time to prepare a bid, improving the accuracy of spare-part orders, or reducing manual work in a shared inbox.

The starting point is measured before automation. This makes it possible to compare the new process with the old one and decide whether the result justifies wider adoption.

## Keep decisions and risk visible

Read access is the default. Permission to change a record, contact a customer, or create a financial commitment is granted for a specific system and action.

Workflows can pause for human approval when the decision involves money, safety, legal terms, or customer responsibility. Every action and approval is recorded with the person and time.

This gives operational owners and governance teams a common view of what the system may do and what remains a human decision.

## Report the measures each team already uses

Depending on the use case, the dashboard can show:

- Service escalations and time to resolution
- Time needed to onboard a technician
- Time to prepare a bid or quote
- Suitable opportunities answered
- Parts returns and order corrections
- Customer questions resolved or transferred
- Workflow exceptions and approval time

Each measure should come from the business process or system that owns it. The dashboard can link back to the recorded cases and workflow runs behind the total.

## Expand only after the first process works

Once one use case produces a useful result, the same approved knowledge, integrations, permissions, and review rules can support another process.

For example, a company may begin with technician support, then add parts identification, customer self-service, and service-call handling. The expansion reuses what has already been connected and governed.

## Where to look next

- [Operations and plant managers](/use-case/ops) — improve handovers and make recurring problems visible.
- [Service managers](/use-case/service) — reduce avoidable escalations and help technicians work independently.
- [AI Workflows](/product/workflows) — see how systems, automated steps, and human approvals connect.
- [Nize Equipment case study](/case-study/nize-equipment) — review measured adoption and service outcomes.

---

## Service managers

Source: https://www.opero.pro/use-case/service

**Fewer avoidable escalations and faster technician support**

Give technicians cited answers from current service knowledge, send difficult cases to specialists with context, and show where information is still missing.

## Senior technicians should solve difficult cases, not repeat familiar answers

Service teams often depend on a few experienced people. Less experienced technicians escalate questions because they cannot find the right manual, bulletin, or previous case quickly enough.

The senior team becomes a bottleneck, customers wait longer, and new employees take months to work independently.

Opero gives every technician access to approved service knowledge by chat or voice. It answers with a visible source and sends uncertain or complex cases to a specialist with the machine details, conversation, and documents already attached.

## Help technicians resolve more cases on the first attempt

Opero searches manuals, service bulletins, parts catalogues, training videos, and past cases for the correct model and serial-number range. It avoids withdrawn instructions and links to the source page behind the answer.

Technicians can check the evidence before acting. If the information is incomplete or the case requires judgement, Opero escalates it instead of guessing.

## Reduce the time needed to train new employees

New technicians can ask the same questions they would normally direct to a senior colleague. They receive the approved procedure and can see the full source in context.

This does not replace practical training or supervision. It gives employees reliable help between training sessions and makes the company’s existing knowledge easier to use in real work.

## Give specialists a better starting point

When a case reaches a senior technician, it can already include the customer, machine, fault, checks completed, sources consulted, and likely next step.

The specialist can focus on the unusual part of the problem rather than asking the customer or colleague to repeat everything.

## See what the team still cannot answer

The service dashboard can show:

- Escalations by team, subject, and product
- Questions Opero could not answer from approved sources
- Documents used most often
- Repeated faults and support themes
- Progress of new technicians toward independent work

Repeated unanswered questions create a practical list of manuals, articles, and training material that need improvement.

## Proven with Nize Equipment

After Nize Equipment introduced the Knowledge Agent, escalations to senior support specialists fell by 60% within 90 days. Technician onboarding became 4.1 times faster, and 92% of the field team used it each week.

Read the [Nize Equipment case study](/case-study/nize-equipment).

## Where to look next

- [Knowledge Agent](/product/knowledge) — give technicians sourced answers in chat or voice.
- [AI Voice Agent](/product/voice) — handle routine calls and transfer difficult ones with context.
- [Construction and material handling](/industry/machinery) — support mobile technicians across models, sites, and languages.

---

## Sales managers & reps

Source: https://www.opero.pro/use-case/sales

**Faster technical answers, quotes, and bid preparation**

Help sales teams answer product questions from approved sources, prepare quotes from current business data, and respond to more suitable opportunities.

## Sales slows down when every technical question needs another department

A customer asks whether a product supports a particular requirement, which part fits an older model, or when an item can be delivered. The sales representative may need to ask engineering, service, parts, or finance before replying.

The delay interrupts the conversation and creates more work for specialists. It can also reduce the customer’s confidence when a competitor answers sooner.

Opero helps the representative find an answer from approved product and company information while the conversation is still active.

## Answer technical questions with a source

A representative can ask in everyday language and receive an answer from product manuals, specifications, service bulletins, and approved internal documents.

The answer links to its source, so the representative can check it and share supporting information with the customer. If the documents do not support a clear answer, Opero can send the question to the correct specialist with the context attached.

## Prepare quotes from current information

Opero can check current products, parts, customer agreements, stock, and pricing rules in connected systems. It can prepare a quote for the representative to review instead of asking them to copy information between several screens.

Prices, discounts, delivery promises, and final customer commitments remain subject to the approval rules you define.

## Respond to more suitable bids

For a large RFP or RFQ, Opero can identify every requirement and prepare standard answers from approved previous bids and company documents.

The bid team receives a structured first draft and can spend its time on the offer, pricing, scope, and customer-specific details. Opero never submits a bid automatically.

## Give specialists fewer, better questions

Engineering, service, and finance should receive the questions that genuinely need their judgement—not every standard product query.

Opero routes exceptions with the customer, opportunity, question, and sources already attached. Their response returns to the sales record or bid section where it is needed.

## Measure the customer delay that was removed

Useful measures include time to a sourced technical answer, time to a reviewed quote, suitable opportunities answered, and questions still waiting for specialist input.

These show whether the sales process became easier for the customer and the team. Counting AI answers alone does not.

## Where to look next

- [Knowledge Agent](/product/knowledge) — answer technical questions from approved company documents.
- [RFP / RFQ Automation](/product/rfp) — prepare a sourced first draft for large bids.
- [Integrations](/product/integrations) — connect customer, product, stock, and pricing systems.

---

## Customer service

Source: https://www.opero.pro/use-case/cs

**Faster answers without losing the human handover**

Resolve routine questions from approved information and transfer complex cases to a person with the conversation, customer details, and sources attached.

## Customers should not wait while an employee searches for an existing answer

Support teams receive questions about products, orders, warranties, service procedures, and known faults. The answer often exists, but it may be spread across manuals, previous cases, email threads, and business systems.

Employees spend time searching and copying. Customers repeat themselves when a case changes channel or moves to another team.

Opero answers routine questions from information your company has approved. When the case needs a person, it transfers the conversation with the customer, product, contract, and sources already attached.

## Give answers that customers and employees can check

Opero can use product documents, service bulletins, customer records, previous cases, and connected order or warranty systems. Each answer links to its source where appropriate.

If the available information is incomplete or the question involves judgement, risk, or an exception, Opero sends it to the right team rather than inventing an answer.

## Use the same context across email, chat, portal, and voice

A customer may start in chat, reply by email, and later call. Opero can keep the relevant context together so the next person sees what the customer asked, what was answered, and what still needs attention.

The customer does not have to explain the entire problem again, and the employee can continue from a useful starting point.

## Handle more than frequently asked questions

Depending on the systems and permissions you connect, Opero can:

- Classify and route incoming requests
- Find an answer in technical or customer documents
- Check order, delivery, contract, or warranty information
- Open or update a support case
- Prepare a reply for review
- Transfer a call or conversation to a person

You decide which actions happen automatically and which require approval.

## Improve the information behind support

The dashboard can show common questions, repeated escalations, unanswered subjects, and the documents used most often.

If customers repeatedly ask the same procedural question, the team can improve the manual, support article, product experience, or training instead of only answering the next ticket.

## Where to look next

- [AI Email Inbox](/product/inbox) — triage incoming messages and prepare replies from company information.
- [Customer Support Portal](/product/portal) — let customers find answers and complete simple actions under your brand.
- [AI Voice Agent](/product/voice) — answer routine calls and transfer difficult ones with context.

---

## Field engineers

Source: https://www.opero.pro/use-case/field

**Answers, service history, and parts at the machine**

Help field engineers find the correct procedure, drawing, service bulletin, and spare part without stopping work to search several systems.

## Field engineers need answers where the work happens

An engineer beside a machine may be wearing gloves, working in poor connectivity, or speaking with a customer. Opening several systems and scrolling through long PDFs is slow and sometimes impractical.

Calling a senior colleague can help, but it interrupts another engineer and may still depend on memory rather than the latest instruction.

Opero lets field engineers ask by voice or chat. It finds information for the machine in front of them and shows the source behind the answer.

## Search using the machine details

The engineer can provide a model, serial number, fault code, symptom, or photo. Opero searches applicable manuals, drawings, service bulletins, previous cases, and parts catalogues.

It narrows the result to the correct machine version and avoids instructions that have been replaced. If the information is not sufficient for a safe answer, it sends the case to a person.

## Keep working in the engineer’s preferred language

Opero supports more than 40 languages. An engineer can ask in one language even when the official service document is written in another.

The explanation is translated, but the citation still opens the original document and page. The engineer can verify the source before continuing.

## Continue from diagnosis to parts and reporting

When the likely failed component is known, Opero can identify the current part number, check stock or warranty details, and prepare an order for approval.

It can also update the service case with the checks completed, work performed, time, and parts used. The office receives a useful record without asking the engineer to re-enter everything at the end of the day.

## Example: an unfamiliar fault at a customer site

An engineer encounters a warning they have not seen before. They ask Opero by voice and state the machine number.

Opero finds the current bulletin, explains the approved checks, and links to the relevant page. The checks point to a sensor, so Opero identifies the correct version and prepares a parts request.

The engineer reviews the information and continues the repair without waiting for someone at the office to search the files.

## Give service managers useful signals

The dashboard shows which questions are common, which faults repeat, and where Opero could not find a reliable answer. This helps managers improve documentation and training around the problems engineers actually encounter.

## Where to look next

- [Knowledge Agent](/product/knowledge) — search company knowledge by chat or voice.
- [Parts & Procurement](/product/parts) — identify and prepare the correct spare part for approval.
- [AI Voice Agent](/product/voice) — use hands-free support in the workshop or field.

---

## Procurement leads

Source: https://www.opero.pro/use-case/procurement

**Faster supplier responses with controlled pricing and approval**

Prepare RFQ answers and purchase requests from approved information while keeping negotiation, spending decisions, and final commitments with your team.

## Procurement teams lose time assembling information that already exists

A request for quotation (RFQ) can contain hundreds of requirements. Preparing a response means finding product details, certificates, previous answers, supplier information, prices, and contract terms across several departments.

At the same time, internal purchase requests may arrive without the correct part, supplier, cost centre, or approval.

Opero prepares the routine information and sends exceptions to the right people. Procurement professionals keep control of negotiation, supplier choice, pricing, and every final commitment.

## Respond to more suitable RFQs

Opero reads the customer’s Word, Excel, or PDF document and builds a checklist of the questions and requirements. It drafts standard answers from approved previous responses and company documents, with a link to each source.

Questions involving price, margin, delivery, legal terms, or missing evidence are flagged for review. Opero does not quote a price or submit the response on its own.

The team can spend more time evaluating the opportunity and negotiating the offer instead of copying standard paragraphs.

## Prepare complete purchase requests

For internal purchasing, Opero can collect the item, quantity, supplier, stock status, contract, cost centre, and reason for purchase before the request reaches an approver.

If information is missing, it asks the requester. If the amount exceeds a limit or the supplier is not approved, it routes the request according to your policy.

The approver receives a complete request with the supporting information attached.

## Keep commercial decisions with people

You define:

- Which documents and previous responses may be used
- Which suppliers and catalogues are approved
- Spending limits and approval levels
- Which price, margin, or contract questions require review
- Who can approve and send an order or RFQ response

Every draft, change, approval, and submission is recorded with the person and time.

## Measure outcomes, not AI activity

Useful measures include the time needed to prepare a first draft, the share of suitable RFQs answered, approval time, purchase requests returned for missing information, and exceptions by supplier or category.

These measures show whether the process improved. The number of AI-generated paragraphs does not.

## Where to look next

- [RFP / RFQ Automation](/product/rfp) — prepare sourced bid responses while people retain final control.
- [Parts & Procurement](/product/parts) — identify parts, check stock and contracts, and prepare orders for approval.
- [AI Workflows](/product/workflows) — connect requests, business rules, systems, and approvers.

---

## Operations & plant managers

Source: https://www.opero.pro/use-case/ops

**Clear handovers, recurring problems, and accountable actions**

Turn shift notes and operational messages into structured records, make recurring faults visible, and keep a reviewable history of what happened.

## Operations teams have plenty of data but still miss important patterns

A shift handover may live in a message thread. A repeated machine fault may be remembered by one engineer. A supplier’s explanation may arrive weeks later by email.

The information exists, but it is difficult to connect. Managers discover recurring problems after another failure, and employees spend time rebuilding the history for audits or investigations.

Opero turns everyday reports, messages, documents, and system records into information the team can search and review.

## Make every handover useful to the next shift

An operator can describe the shift by voice or text. Opero can structure the report with the site, line, equipment, fault code, action taken, unresolved issue, and person responsible for the follow-up.

The next shift receives a consistent record rather than several incomplete messages. Missing information can be requested before the handover is closed.

## See recurring faults before they become normal

Opero can connect reports that refer to the same machine, fault, or process. When the same problem appears repeatedly, it can notify the maintenance or operations team and attach the previous occurrences.

This does not replace production or maintenance systems. It helps the team see the explanation and actions around the numbers those systems already record.

## Find the history behind an operational event

Managers can search maintenance bulletins, supplier notes, operating procedures, handovers, and previous cases together.

For example, an auditor asking what happened on a particular shift can receive the structured handover, the source messages, the actions completed, and the people who approved them. The answer comes from recorded events rather than memory.

## Automate follow-up without removing responsibility

Opero can create a maintenance case, request missing details, notify the next shift, or prepare a report. You choose which steps run automatically and which require a named person to approve them.

Every action and approval is recorded, making it clear who decided what and when.

## Focus the dashboard on operational questions

Useful views include:

- Repeated faults by machine, line, and site
- Incomplete handovers and overdue follow-ups
- Questions that could not be answered from current procedures
- Actions waiting for approval
- Procedures or documents used most often

Production output and downtime should continue to come from the systems that measure them. Opero adds the context needed to understand and address the causes.

## Where to look next

- [AI Workflows](/product/workflows) — structure handovers, follow-ups, and approval steps.
- [Knowledge Agent](/product/knowledge) — search procedures, supplier notes, and maintenance history with sources.
- [Industrial production machinery](/industry/production) — support fault diagnosis and maintenance across production equipment.

# Case studies

---

## How Nize Equipment cut L2 escalations by 60% in 90 days

Source: https://www.opero.pro/case-study/nize-equipment

**Customer:** Nize Equipment · **Published:** 2026-05-02

## Customer quote

> A technician with limited product knowledge found the right solution through the Opero AI agent and resolved the issue without interrupting a colleague who was out with customers.
>
> — Mette Meyer Thuesen, Service & IT-manager, Nize Equipment

## Metrics

| Value | Label |
| --- | --- |
| 60% | reduction in L2 escalations |
| 4.1× | faster onboarding for new techs |
| < 30s | median time to first useful answer |
| 92% | technician adoption (weekly active) |

## The starting point

### Eighteen years of service knowledge — none of it searchable

Nize Equipment sells and services large-format print and finishing machinery across the Nordics. Their service organisation is small by design: lean, senior, and constantly on the road. When a new technician joins, the ramp-up curve is steep — most of the institutional knowledge lives in PDFs scattered across a network drive, in OEM service bulletins emailed from Europe, and in the heads of two or three senior engineers.

In 2024 the service-and-IT lead, Mette Meyer Thuesen, mapped the cost of that fragmentation. Roughly one in three field calls became a phone call back to a senior engineer. Onboarding a new technician took six months before they were billable on complex jobs. Customer downtime was the visible KPI; internal escalations were the hidden one.

## The approach

### Index every manual. Cite every answer. Put it in front of the engineer.

We started with the corpus: 1,400 service documents across three OEM product families, two languages, and roughly twelve years of revision history. The first deployment was an internal-only agent — chat in the browser, citations back to the source PDF, and a feedback widget on every answer.

Within four weeks the agent was answering questions a technician would otherwise have escalated. The interesting work was downstream: tagging revisions so the agent never cites a superseded bulletin, surfacing model-specific schematics, and adding a voice mode so engineers on the floor could ask hands-free.

## The result

### A 60% drop in escalations, and a senior team that gets to think again

Ninety days in, L2 escalations had dropped by 60%. New hires were reaching first-call independence in roughly six weeks instead of six months. The senior engineers — the ones who used to be the bottleneck — got their afternoons back.

Phase two added the customer portal: end-customers ask the same agent before opening a ticket. Phase three, currently in pilot, adds parts ordering: when the agent identifies the failing component, it pre-fills an order against the right SKU. Mette's team is the customer of the agent. Their customers are the customer of the portal. The senior engineers are the customer of their own time back.

# Resources

---

## What is a CMMS? A plain explanation for maintenance teams

Source: https://www.opero.pro/resources/what-is-a-cmms

**Kind:** guide · **Read time:** 6 min · **Author:** Opero · **Tag:** Glossary · **Published:** 2026-10-06

## TL;DR

- **Definition** — a CMMS is software that keeps track of your machines, the work done on them, the parts used and the maintenance that is due.
- **What it is good at** — work orders, schedules, parts stock and history. The record of what happened.
- **What it is not** — it does not tell a technician how to do the repair. That knowledge lives in manuals, in people and, with Opero, in a knowledge base connected to the CMMS.

## CMMS meaning

CMMS stands for computerised maintenance management system. It is the software a maintenance department uses to keep track of its machines and the work done on them.

A CMMS answers questions like these: Which machines do we have? What is due for service this month? Who is working on the broken conveyor? Which parts did we use on it last time? How many hours has line 2 been down this year?

Well-known products include MaintainX, eMaint, Fiix, IBM Maximo and the maintenance modules of SAP and Microsoft Dynamics. Some companies still run their maintenance from spreadsheets. A CMMS replaces those spreadsheets with one system that everyone can see.

## What a CMMS keeps track of

Most systems have the same core parts:

- **Assets.** A list of your machines and equipment, often with location, serial number and supplier.
- **Work orders.** A ticket for each job: what needs to be done, on which asset, by whom, by when. Work orders are the heart of the system.
- **Preventive maintenance schedules.** Recurring jobs that the system creates automatically, every 500 running hours or every three months, for example.
- **Spare parts and inventory.** What is in the storeroom, where it is, and when to reorder.
- **History.** Every completed work order, with time spent and parts used. Over the years this becomes the maintenance record of each machine.
- **Reports.** Downtime per machine, cost per asset, open work orders, overdue preventive maintenance.

## CMMS versus EAM versus ERP

The three terms overlap and sales teams use them loosely.

A **CMMS** is about maintenance. Work orders, schedules, parts.

An **EAM** (enterprise asset management) system covers the whole life of an asset: buying it, installing it, maintaining it, replacing it. In practice, many EAM systems are a CMMS with finance and procurement features added.

An **ERP** (enterprise resource planning) system runs the business: orders, invoices, stock, accounting. Large ERP systems have a maintenance module, which is why some companies use SAP as their CMMS.

If you are a maintenance manager choosing a tool, you most likely need a CMMS. If finance already runs an ERP with a maintenance module, you may already have one.

## What a CMMS does well

A good CMMS gives you three things. Order, because every job is a work order with an owner and a date. Memory, because the history of each machine is in one place. Discipline, because preventive maintenance comes up on time whether or not someone remembers it.

Plants that go from spreadsheets to a CMMS usually see fewer missed services and a clearer picture of which machines cost the most.

## What a CMMS does not do

A CMMS records that a repair happened. It does not explain how to do it.

Open a typical closed work order and you find: "Replaced sensor, tested OK." Which sensor? Which setting? What did the technician check first? That knowledge went home with the technician.

The CMMS also does not read your manuals, your supplier's service bulletins or the email where a colleague described a workaround. When a technician stands at a stopped machine, the CMMS tells them the machine has stopped five times this year. It does not tell them what to do now.

That gap is where most unplanned downtime hides. The record exists, the knowledge does not.

## How AI fits next to a CMMS

The useful place for AI is on top of the CMMS, not instead of it.

Opero connects to your CMMS, reads the work orders and history, and connects them to your manuals, drawings, service bulletins and recorded repairs. A technician asks, in plain words, "the filling machine shows fault 31, what do I do?" and gets the steps with the source next to them: the manual page, the last work order on that fault, or a video a colleague recorded.

When the job is done, the technician films or dictates a short report. Opero writes it up and sends it to the CMMS as a finished work order, with the parts and the steps. The next person who meets fault 31 finds a real answer instead of "tested OK."

The CMMS stays the system of record. Opero makes the record useful at the machine.

## Questions people ask about CMMS

**Do we need a CMMS before we can use AI?** No. Opero also works from manuals, documents and recorded repairs alone. A CMMS adds the maintenance history, which makes the answers better.

**Which CMMS does Opero connect to?** SAP, Microsoft Dynamics, ServiceNow, IFS and others. See the [integrations page](/product/integrations) for the current list.

**Is a work order the same as a service report?** A work order is the request and the plan. A service report is what was actually done. In many plants the second one is thin or missing, and that is the part Opero helps with.

If you want to see Opero next to your own CMMS, [book a 20-minute demo](/contact).

---

## What is a standard operating procedure (SOP)?

Source: https://www.opero.pro/resources/what-is-a-standard-operating-procedure

**Kind:** guide · **Read time:** 6 min · **Author:** Opero · **Tag:** Glossary · **Published:** 2026-10-06

## TL;DR

- **Definition** — an SOP is a step-by-step instruction for one job, written so that a trained person can do the job the same way every time.
- **Why they fail** — they are long, they live in a binder nobody opens, and they go out of date when the machine or the method changes.
- **The fix** — short steps with a picture each, kept where people work, updated from a video when the job changes.

## Standard operating procedure meaning

A standard operating procedure, or SOP, is a written instruction that explains how to do one job, step by step, so that everyone does it the same way.

"Standard" means agreed. The procedure is the way the company has decided this job is done. "Operating" means it is about doing the work, not about policy. "Procedure" means steps in order.

SOPs exist in every industry. In a plant you find them for machine start-up, changeovers, cleaning, lockout and tagout, quality checks and many repairs. In a service company you find them for commissioning, inspections and standard fault fixes.

## What goes into an SOP

A usable SOP has six parts:

1. **Title and scope.** Which job, on which machine or product. "Replace the output shaft seal on the gearbox of line 3."
2. **Who does it.** Required role or training.
3. **Safety.** What to switch off, lock, wear or check before starting.
4. **Tools and parts.** Everything to collect before the first step.
5. **The steps.** Short, numbered, one action each, in the order they happen. A picture or a short video clip for every step where it helps.
6. **How to check the result.** What "done" looks like. Run the machine and confirm there are no further leaks.

The steps are the part people read. Everything else exists to make the steps safe and complete.

## An example: resetting a drive fault

Here is a short SOP for a common job in an electrical cabinet, written the way a technician would say it:

**Reset a frequency drive after an overcurrent fault (fault F0001)**

Before you start: switch off the main power at the cabinet and wait five minutes for the capacitors to discharge. Wear insulated gloves.

1. Open the cabinet door and find the drive for motor M3.
2. Check the motor cable connections. Press each terminal. If one is loose, tighten it.
3. Switch the main power back on.
4. Press the reset button on the drive. The display shows "Ready."
5. Start the motor from the control panel and watch the current for one minute. It should stay below 12 A.

If the fault comes back within a minute, stop. The problem is in the motor or the load, not in the drive. Open a work order.

That is five steps and about 100 words. Most real SOPs are longer, but the shape is the same.

## Why most SOPs are not used

Ask a technician where the SOP for a job is, and you often get one of three answers: in a binder in the office, in a PDF somewhere on the network drive, or "we never wrote one, ask Jens."

Three things kill SOPs:

- **They are too long.** A twelve-page document for a twenty-minute job will not be read at the machine.
- **They are not where the work is.** If the SOP is in the office and the job is on the floor, people work from memory.
- **They go out of date.** The machine gets a new control unit, the method changes, and the SOP still describes the old way. After the second wrong instruction, people stop trusting the document.

A good SOP is short, reachable from a phone at the machine, and updated the same week the job changes.

## SOPs and work instructions

The terms are close. A work instruction is usually more detailed and more specific to one machine or one workstation. An SOP may cover a type of job across several machines. In practice many companies use the words for the same thing. What matters is that the document has the six parts above and that people can find it.

## How to make SOPs from a video

The fastest way to write an SOP is not to write it. Film it.

An experienced technician does the job once with a phone on a stand or in a colleague's hand, and talks while working. "Now I check the three motor terminals. This one is loose, so I tighten it to 2.5 newton metres."

Opero takes that video and turns it into a step-by-step guide: one step per action, a frame from the video as the picture, and the spoken words as the instruction. The technician checks it, fixes anything wrong, and it goes into the knowledge base where the next person can find it from their phone, at the machine.

When the job changes, someone films it again. The SOP is updated in an afternoon instead of waiting a year for the next document review.

This is also how a company keeps the knowledge of people who are about to retire. One afternoon of filming captures more than a month of interviews.

## Questions people ask about SOPs

**How long should an SOP be?** As short as the job allows. If a step needs a paragraph, it is probably two steps.

**Who writes SOPs?** The person who does the job best, with someone else checking it for safety and clarity. Not the office.

**How often should they be reviewed?** Every time the job changes, and at least once a year. With video it is cheap enough to do.

If you want to see a video turned into a procedure your team can follow, [book a 20-minute demo](/contact).

---

## What is machine downtime, and what does it really cost?

Source: https://www.opero.pro/resources/what-is-machine-downtime

**Kind:** guide · **Read time:** 6 min · **Author:** Opero · **Tag:** Glossary · **Published:** 2026-10-06

## TL;DR

- **Definition** — machine downtime is any period when a machine should be producing and is not.
- **Two kinds** — planned (maintenance, changeovers) and unplanned (breakdowns, missing parts, waiting for the one person who knows the fix).
- **The cost** — lost output per hour, plus labour standing idle, plus late deliveries. Most plants count the first and forget the other two.

## What machine downtime means

Machine downtime is the time a machine is not running when the production plan says it should be. A press that is being repaired is down. A packaging line that waits for a technician is down. A CNC machine that stands still because the operator is looking for the manual is also down, even though nothing is broken.

The word covers both the short stops that happen ten times a day and the long breakdown that takes a shift to repair. Both count. The short ones are often the bigger total.

## Planned and unplanned downtime

Planned downtime is time you decided to stop. Preventive maintenance, changeovers between products, cleaning, software updates. You can schedule it when it hurts least, and you can shorten it with good procedures.

Unplanned downtime is time the machine took from you. A bearing fails. A sensor gives a false alarm. A fault code appears that nobody on the night shift has seen before. This is the kind that costs the most, because nobody was ready for it.

A useful rule: planned downtime is a cost you manage, unplanned downtime is a cost you discover.

## How to measure it

Keep it simple. For each machine, record three things per stop:

- When it stopped and when it ran again
- Why it stopped, in one line
- Who fixed it and what they did

From that you get total downtime per machine, per shift and per cause. Most CMMS systems record the first two if people fill them in. The third one, what was actually done, is the part that is usually missing, and it is the part that saves time the next time the same fault appears.

If you use OEE (overall equipment effectiveness), downtime is the availability part of the formula. Availability is run time divided by planned production time. A line planned for 8 hours that ran 6.5 hours has an availability of 81 percent.

## What an hour of downtime costs

Add three numbers:

1. **Lost output.** Units per hour multiplied by the margin per unit. If the line makes 400 units an hour and you earn 5 euros on each, one hour costs 2,000 euros in margin alone.
2. **Idle labour.** The operators and technicians who are paid while the machine stands still.
3. **Knock-on cost.** Overtime to catch up, late deliveries, penalties, scrap from a bad restart.

Most plants know the first number. Few add the second and third. When you add them, an hour of unplanned downtime on a main line is often two to three times what people assumed.

## The four causes behind most unplanned downtime

In the plants we work with, four causes come up again and again:

**1. The fault is known, but not by the person on shift.** The machine stopped with the same fault last year. Someone fixed it in twenty minutes. That person is on holiday, and nobody wrote down what they did. The fix takes three hours this time.

**2. The information exists but is scattered.** The manual is in SharePoint, the maintenance history is in the CMMS, the supplier's workaround is in an email from 2023. Finding all three takes longer than the repair.

**3. Waiting for a part.** The technician finds the fault quickly, then spends an hour working out the right part number and whether it is in stock.

**4. Reports are not written, so the pattern is never seen.** When a stop is not recorded properly, nobody notices that the same machine has stopped for the same reason six times this quarter.

Only the third cause is about logistics. The other three are about knowledge: having it, finding it and keeping it.

## How to reduce machine downtime

Start with the repeat faults. Pull the stops from the last six months, group them by machine and cause, and look at the top five. They are usually a large share of the total, and each one already has a known fix somewhere in the company.

Then make the fix easy to find at the machine. A technician on the night shift should be able to ask "line 3 stops with fault E-12, what do I do?" and get the steps, with the manual page or the previous repair next to them. That is the problem Opero was built for. Technicians ask by chat, voice or video, and the answer comes from your own manuals, CMMS history and recorded repairs.

Finally, record what was done, every time. The fastest way is a short video of the repair. The technician films the fix, and Opero turns it into a written report that goes into the CMMS and into the knowledge base. The next person who meets the fault finds it in seconds.

## Related terms

- **MTTR, mean time to repair.** The average time from stop to restart. Knowledge problems make this number long.
- **MTBF, mean time between failures.** The average running time between two stops. Preventive maintenance improves this number.
- **OEE, overall equipment effectiveness.** Availability multiplied by performance multiplied by quality. Downtime lowers availability.
- **Preventive maintenance.** Scheduled work done to stop a failure before it happens.

If you want to see how this works on one of your own machines, [book a 20-minute demo](/contact).

---

## Find documents faster with automatic folders

Source: https://www.opero.pro/resources/2026-08-31-document-folders

**Kind:** blog · **Read time:** 5 min · **Author:** Opero · **Tag:** Product Update · **Published:** 2026-08-31

## TL;DR

- **Opero builds the folders** — it reads each file and groups it by subject. You do not create folders yourself.
- **Two clicks to the file** — pick a category in the first column, narrow it in the second, and the file is in the third.
- **A file can sit in more than one folder** — a manual that covers four models shows up under all four.

A document library grows fast: manuals, drawings, specifications and reports. In one long list, search helps when you know what to type, but browsing is hard.

You can now browse documents in folders, and Opero creates the folders for you.

<figure style="margin:32px 0;">
  <a href="https://www.youtube.com/watch?v=5_0UJjOp9v4" style="display:block;width:100%;max-width:860px;border-radius:14px;overflow:hidden;border:1px solid var(--color-op-line);background:#000;">
    <img src="/updates/documents-folders-drag.gif" alt="A document being moved to a different automatically generated folder in Opero" loading="lazy" style="display:block;width:100%;height:auto;" />
  </a>
  <figcaption style="margin-top:10px;font-size:13px;color:var(--color-op-mute);">
    A file is dragged from one folder to another. <a href="https://www.youtube.com/watch?v=5_0UJjOp9v4">Watch the full video on YouTube</a>.
  </figcaption>
</figure>

## Opero creates the folders from your files

Opero reads every document you upload and works out what it is about. For a service team that is the equipment brand and the model. For a legal team it is the counterparty and the contract type. In engineering, the product line and the component.

Opero uses those details as folders. Your team does not need to design a folder structure or file every new upload by hand.

## Narrow the list in two steps

![The folders view. A Manufacturer column with CMP selected, a Model in CMP column beside it, and the matching documents on the right with Also-in pills under each file name.]

The first column shows a broad category. The second makes it more specific. The matching files appear on the right.

In the picture, that is manufacturer then model. Click a manufacturer and the second column fills with only its models. Click a model and you are down to a handful of files. A count sits next to every folder, so you know what you are about to open.

Each column also has a filter. Type a few letters to shorten a long list.

A breadcrumb across the top shows where you are. Click any part of it to go back up.

## A file can live in more than one folder

One document may belong in several places. A manual might cover four models, or a contract might cover three products. Opero shows the same file in every relevant folder.

Under each file name, the **Also in** labels show every other folder that contains the file.

## Drag a file to move it

![A document card lifted off the list and dragged over the Manufacturer column, on its way to a different folder.]

If a file is in the wrong place, drag it to the right folder. Opero asks you to confirm the change first, so an accidental drag does no harm.

## Rename in place, merge by dropping

Over time, the same folder name may appear with different spellings. You can fix that without leaving the folder view:

- **Rename.** Click the folder name and type. Every file under it follows.
- **Merge.** Drag one folder onto another and they become one. Both spellings, one folder, all the files.

## It works on a phone

The folder view stacks into one column on a small screen. Tap a category, tap the next one, tap the file.

This makes the folder view easy to use away from a desk.

## Take the files with you

Select several documents to download them as **one zip**. This is useful before a trip, an audit, or anywhere with a poor connection.

When you download one file from the viewer, Opero keeps its original file name.

## The AI reads the same folders

The chat agent can use the folders too. When you name a product, customer, or other category in your question, it can search the relevant folder instead of the whole library.

## Where to find it

Open **Documents** and use the **List / Folders** toggle on the right of the search box.

Use the list when you know what to search for. Use folders when you want to browse by category.

---

## What’s new in Opero, Summer 2026: eight problems we made easier

Source: https://www.opero.pro/resources/2026-08-31-product-update

**Kind:** blog · **Read time:** 5 min · **Author:** Opero · **Tag:** Product Update · **Published:** 2026-08-31

## TL;DR

- **Turn repeated work into a workflow** — build the process on a visual canvas, or describe the outcome and let AI create a draft for your team to review.
- **Give each team an AI email inbox** — triage incoming messages, skip spam, qualify leads, handle support requests, and draft informed replies using company knowledge.
- **Make training videos searchable** — add videos to the knowledge base so people can ask questions and open the exact moment that supports each answer.
- **Talk to Opero without making a call** — use the voice agent directly in the browser when typing is inconvenient, with the same knowledge and language settings as chat.
- **Keep complete Excel files intact** — open and search spreadsheets without losing their sheet tabs, formulas, formatting, or relationships between sheets.
- **Describe the problem and let AI build the workflow** — Opero chooses the steps, adds the logic, and connects the actions; you review the workflow and decide when to activate it.
- **Find gaps in company training** — anonymous analytics show repeated questions and common topics, helping admins decide what to add to training and documentation.

Product updates are usually a list of shiny new buttons. Ours is about work your team should not have to keep doing by hand.

This summer, we focused on eight common problems: repetitive processes, hard-to-find documents, overloaded inboxes, training trapped in videos, inconvenient typing, spreadsheets that lose their meaning, automations that are difficult to design, and gaps in company training.

## 1. Turn a repeated process into a workflow

Many jobs follow a predictable sequence: information arrives, the team checks it against company rules and systems, someone makes a decision, and the result is recorded or sent to the next person. The process is clear, but carrying it out by hand is slow and easy to get wrong.

Opero turns that sequence into a visual workflow. Add the steps to a canvas, connect them, and choose where a person needs to review or approve the result. [See how the workflow builder works](/resources/2026-08-31-workflow-builder).

## 2. Describe the problem instead of designing the automation

The person who understands a business process is not always the person who knows how to automate it. Requiring them to choose every technical action and connection can stop a useful idea before it gets started.

Tell Opero the result you need in plain language. It works out the basic logic, chooses the relevant actions, and connects the steps on the workflow canvas. You review the draft, add company-specific rules or approvals, and activate it only when it is ready.

This lets the person closest to the problem create a practical first version without having to design the technical solution first.

<figure class="update-wide-demo">
  ![A request describing a job-completion and invoicing process, followed by the complete workflow Opero created from it]
  <figcaption>Describe the outcome in plain language. Opero turns it into a connected workflow your team can review, edit, and activate.</figcaption>
</figure>

## 3. Find documents without maintaining folders

Document libraries rarely stay organised. Someone has to decide where every file belongs, create folders, rename files, and update the structure when the business changes.

Opero reads the documents and groups them by useful information such as manufacturer, model, product type, or subject. Your team can browse the collection like a set of folders, but nobody has to build or maintain those folders manually. [See how automatic document folders work](/resources/2026-08-31-document-folders).

<figure class="update-wide-demo">
  ![Opero document folders grouped first by manufacturer and then by model]
  <figcaption>Choose a category in the first column, narrow it in the second, and open the matching document.</figcaption>
</figure>

## 4. Handle incoming email without turning the inbox into a full-time job

An inbox is not one problem. It may contain support requests, sales enquiries, supplier questions, internal requests, mailing lists, and spam. Sorting those messages takes time before the real work has even started.

Give each team an Opero email address on your own domain. Opero can triage incoming messages, skip spam, qualify a sales lead, identify a support request, and route the message to the right workflow or person. When a reply is needed, it can check your documentation, connected systems, and useful previous conversations before preparing the answer.

You choose how far it goes. Opero can save a draft for review or send the reply automatically when the rules and evidence are clear.

Useful information no longer has to stay trapped in the inbox:

- **Save attachments.** Add a specification, drawing, label photo, or signed contract to your knowledge base in one click.
- **Save whole threads.** Turn a useful email conversation into a document, so the answer is available outside one person's inbox.

Replies go out from the inbox, with the people already on the thread filled into Cc.

## 5. Make the information inside training videos searchable

Companies record product demonstrations, safety briefings, walkthroughs, and internal training sessions. But finding one answer inside a long recording is difficult, so people often watch the whole video or ask the same expert again.

Upload the video to Opero and it becomes part of the knowledge base alongside your documents. People can ask questions in their own words. Opero answers from the relevant section and cites the exact second where the information appears, so they can open the source and check it immediately.

## 6. Get help when typing is inconvenient

Chat works well at a desk. It works less well beside a machine, on a factory floor, or whenever your hands are occupied.

Press the voice button and ask Opero directly in the browser. There is no phone call to make and nothing to install. The browser voice agent uses the same company knowledge as chat.

It also supports more languages and remembers each team's preferred language for future calls and chats.

## 7. Understand a spreadsheet without losing its structure

A spreadsheet is more than a table of values. Formulas explain how numbers were calculated. Sheet tabs separate different parts of the business. Colours and formatting often carry meaning too.

Before, Opero displayed spreadsheets as plain tables. That made the values readable but removed some of the context needed to understand them correctly.

Opero now opens complete `.xlsx` files with their sheets, formulas, formatting, and relationships intact. When you ask a question or ask AI to complete a column, it can use the structure of the workbook rather than only the visible cell values.

## 8. Find out what your company needs to explain better

When employees repeatedly ask the same question, the problem may not be the employee. The procedure may be unclear, difficult to find, or missing from company training.

The admin dashboard shows anonymous usage patterns without exposing individual conversations. Admins can see how often Opero is used, which subjects generate the most questions, which questions appear repeatedly, and where the knowledge base may be missing useful information.

If many people ask how to complete the same procedure, the company knows that subject needs clearer documentation or training. The dashboard helps answer two questions: what does the team need today, and what should the company explain better tomorrow?

## What problem should we solve next?

The best automation ideas usually begin with: “Why are we still doing this by hand?”

If your team repeatedly copies information, searches for the same answer, or waits for the same approval, [tell us about the process](/contact). We may be able to turn it into a workflow—or remove the unnecessary work entirely.

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---

## Build a workflow yourself, or ask AI to start it

Source: https://www.opero.pro/resources/2026-08-31-workflow-builder

**Kind:** blog · **Read time:** 6 min · **Author:** Opero · **Tag:** Product Update · **Published:** 2026-08-31

## Metrics

| Value | Label |
| --- | --- |
| 100+ | Ready-made actions |
| 11 | Block types |
| 0 | Lines of code |

## TL;DR

- **Build it on a canvas** — add the steps, connect them, and click Activate. No code or config file needed.
- **100+ ready actions** — every action Opero can already do in SAP, Salesforce, ServiceNow, SharePoint, HubSpot and Teams is now a block you can drop on the board.
- **AI reads the messy parts** — the AI block pulls the useful details out of a PDF, form, or long email.
- **People stay in control** — an approval step can pause the workflow until someone approves it.
- **You see every run** — live step list, plain-language failures, a link you can paste to a colleague, and a Slack ping when a run breaks.
- **Start with an AI draft** — describe what you want in one sentence and the chat agent creates a workflow for you to review.

Every company has repetitive work: an email arrives, someone copies details into another system, checks the result, and sends a reply. The next day, they do it all again.

Opero can now handle jobs like these. You can build the steps on a canvas, or describe what you need and let AI create a first draft.

## What a workflow is

A workflow is simply a list of steps that Opero follows for you. It has three parts:

1. **A start.** Something happens. An email lands. The clock hits 7am. A record changes in one of your systems.
2. **Some steps.** Read a record. Ask the AI to read a PDF. Write a record somewhere else. Send an email.
3. **A result.** For example, a record is updated or an email is sent.

You put these parts together on one screen.

## A service workflow in Opero

This illustrative service workflow combines customer and parts lookups, relevant documentation, a drafted reply and an approval step for urgent requests.

## The canvas

Each step is a block on the board. Every block shows its type, its name and a short note on what it does. Lines join the blocks. The lines show which step comes next.

Some blocks split the path. In the example above, urgent requests go to a service manager for review. Standard requests go straight to a reply.

You pick fields from a list. There is no JSON to write and no YAML to get wrong.

You can undo or redo changes. An incomplete block gets a coloured dot, and Opero will not activate the workflow until every required field is complete.

## Choose what each step should do

Opero already talks to the systems your team runs on. CRMs, finance, ticketing, project tools, document stores.

Each action is available as a block. There are more than 100 to choose from.

Blocks are grouped by system. Choose a system, then choose an action such as “check parts and warranty.” Opero shows the fields that action needs and tells you if anything is missing before you run it.

## Use AI for unstructured information

Real work is messy. A report is a PDF with a table in it. An email has the order number buried in the fourth paragraph. A form comes in with the fields half filled.

The **Agent brain** block reads that information and pulls out the fields you need, such as quantities, line items, and dates. The next step receives clean, structured data.

## Add approval where it matters

Some steps should never happen on their own. Sending a customer an invoice is one. Committing a purchase order is another.

Add an **Approval gate** and the workflow pauses until someone approves or rejects it. If they approve, it continues from the same place.

Opero does not allow delete actions in a workflow, so an automated run cannot erase a record.

## See what happens when it runs

You can follow every workflow run:

- Every run shows a live list of its steps as they happen.
- A failed run gets a plain-language card that says what went wrong, not a stack trace.
- Opero posts to your Slack channel when a run fails.
- Every run has its own link. Paste it to a colleague and they open the same view you are looking at.
- Run history pages back through everything, not just the last twenty.

## Three ways to start a workflow

- **On a clock.** Pick a time and a timezone. The timezone box fills itself in from your browser, so 7am means 7am where you are.
- **From an email.** Your workflow gets an address and starts when it receives a message. It ignores automatic out-of-office replies.
- **On a system event.** A record changes in your CRM, or an order lands in your store, and the workflow starts on its own.

## Or ask AI to create a first draft

Tell the chat agent what you want in a sentence. It creates a draft with the blocks already connected. You review it, fill in any missing details, and switch it on.

## An example

Here is the workflow shown near the top of this page. A service request arrives by email. Opero finds the customer account and its SLA in the CRM. An AI agent sorts the request by fault, equipment and urgency.

Opero then finds service guidance in the knowledge base. It checks parts and warranty in the ERP. A second AI agent drafts a reply with sources and part options.

An urgent request waits for a service manager to approve it. A standard request gets the reply and a CRM case update at once.

One team sends the request. Another reviews the draft. Nobody has to type the same information twice.

## Where to find it

Open **Workflows** in the left menu. Start with a manual trigger and two blocks, press Run now, and watch the steps light up.

When it works as expected, replace the manual trigger with a schedule.

---

## AI for MEP contractors: answers from the project record

Source: https://www.opero.pro/resources/ai-for-mep-contractors

**Kind:** blog · **Read time:** 6 min · **Author:** Opero · **Persona:** Service mgr · **Tag:** Operations · **Industry:** MEP · **Published:** 2026-08-10

MEP contracting services run on records. Every project produces a handover binder, a subcontractor list, a punchlist, and a commissioning sign-off. The service call six months later depends on those records. Most firms store them well. Very few can retrieve them at the speed of a phone call.

This article explains what an AI assistant must do to be useful in MEP contract work, why generic AI tools fail at it, and what changes for a service team when retrieval works.

## What an MEP service call needs to know

A technician calls in about a trip on a ventilation system. To answer with confidence, the person on the other end needs four things:

1. **The as-built record for that project.** Not the design drawings. The record of what was installed, by which subcontractor, with which setpoints.
2. **The commissioning evidence.** What was tested, when, and what the sign-off values were.
3. **The code that applies to that site.** Codes differ by jurisdiction and by revision date. The contract fixes which version applies.
4. **The open punchlist.** A known open item often explains the fault.

A senior service engineer holds most of this in memory. That is the problem. When the engineer retires or moves on, the recall goes with them. New hires spend months building it back.

## Why generic AI fails on MEP projects

A generic chatbot pointed at a folder of project documents breaks in a specific way: it cannot scope. Two projects share an equipment model, and the model mixes their records. The unit installed at Project X in 2022 is not the unit at Project Y in 2024. Different commissioning, different setpoints, different code revision, different subcontractor.

Code references break the same way. A generic model quotes the most common public version of a standard. The contract for the site in front of the technician may run under a regional variant from an earlier revision. A confident answer from the wrong code version is worse than no answer.

The fix is retrieval that scopes before it searches. The question carries a project. The system narrows to that project's records, that jurisdiction's code version, and that contract's subcontractor list first. Only then does the language model see the candidates.

## Subcontractor records are part of the answer

MEP subcontractors do much of the installed work. Their documentation quality varies, and their records arrive in every format: email threads, scanned certificates, photos of nameplates. When a fault traces back to subcontracted work, the service team needs to know which subcontractor installed it, what they handed over, and what the warranty terms in the subcontract say.

An assistant that indexes subcontractor handovers at ingest, tagged by project and by trade, turns that search from an afternoon into a question. It also changes how the next MEP contract gets managed: gaps in a subcontractor's handover show up at project close, not at the first service call.

## Before and after: the first call

**Before.** The technician calls the office. The coordinator asks a senior engineer. The engineer remembers the project, roughly. Someone opens the project drive and searches through folders. The technician waits on site, or leaves and returns another day. Elapsed time: hours, sometimes a second visit.

**After.** The technician asks the assistant, by voice or by chat. The answer comes back scoped to the project, with the commissioning record and the relevant code clause cited. The senior engineer gets involved only when the retrieval surfaces a gap in the record. Elapsed time: minutes, on the first visit.

At one of the Nordics' larger MEP contractors, this is the deployed pattern today. The adoption signal is simple: technicians come back and ask a second time.

## What to look for in AI for MEP contractors

If you evaluate AI for an MEP contracting business, test for these four things:

- **Project scoping.** Ask about two projects that share an equipment model. The answers must not mix.
- **Jurisdiction awareness.** Ask for a code reference. The answer must cite the version that applies to the site, not the most popular one.
- **Structured handover capture.** A voice handover from a commissioning engineer must become a queryable record, tagged with the project and the equipment IDs.
- **An audit log.** Every retrieval must be traceable. Quality teams and dispute processes depend on it.

Opero is built around these requirements. Read how it works for building technical services on the [MEP industry page](/industry/mep), or [book a demo](/demo) and test it against your own project records.

---

## 13 Things I Learned Building AI Agents for Technical Field Service

Source: https://www.opero.pro/resources/13-things-building-ai-agents

**Kind:** blog · **Read time:** 10 min · **Author:** Redouane Achouri · **Tag:** Engineering · **Published:** 2026-08-02

At [Opero](/) we build agents, the sort of voicebots and chatbots technical staff use in the field or at the office while preparing for a job. They are built on the technical documentation of manufacturers, engineering labs, HVAC companies, and field service teams.

These agents are not demonstrations. A technician uses them when a machine is broken.

Many of my first beliefs were incorrect. Some were also expensive. These are the 13 things I learned along the way.

It would mean the world if this is useful to someone building their own agents.

---

## The Stack

This is the software that we use:

- **Agent and API:** Python, [FastAPI](https://fastapi.tiangolo.com/)
- **Orchestration:** [LangGraph](https://www.langchain.com/langgraph) for the graph, [LangChain](https://www.langchain.com/) for the components
- **Observability:** [Langfuse](https://langfuse.com/)
- **Retrieval:** OpenAI embeddings, [Milvus](https://milvus.io/) (hybrid dense and sparse search), [zerank-2](https://www.zeroentropy.dev/) for reranking
- **Structured data:** Postgres
- **Models:** Different providers with automatic failover. If one provider gives an API error during operation, we send the request to a different provider.
- **Communications:** agentic phone calls, SMS, and email with [Hail](https://hail.so).
- **Infrastructure:** servers on [Hetzner](https://www.hetzner.com/), storage and some pipeline components on AWS

![System overview — the whole graph before the thirteen parts](../../assets/13-things/fig-00-system-overview.svg)

---

## Part 1. Ingestion

### Lesson 1: Build Your Own Document ETL Pipeline

![Own the ETL pipeline — the foundation cannot be a black box](../../assets/13-things/fig-01-own-the-etl.svg)

We started with a commercial document processing platform called "Unstructured".

The platform was easy to start. We had a system in operation in less than one day. Commercial platforms are good for this.

Then we found problems.

The extraction quality was low. The platform gave structured output, but the structure was correct for Unstructured, not for us. We changed our system to agree with their format. This is the incorrect sequence. It's an anti-pattern. We had to twist our pipeline to make it work.

Then we started working with Zeppelin (a major [Caterpillar](https://www.caterpillar.com/) dealer), and the first document they shared was 8,000 pages of dense technical documentation. Drawings, schematics, backlinks that cross-reference other pages, complex identifiers, etc.

The pipeline on Unstructured failed 20 times. We paid for each failure.

At that time, we made a decision. The cost was one problem, but the larger problem was different: our document processing logic was in a system that we could not examine, repair, or improve.

We benchmarked the available platforms and libraries, and settled on an open source library called Docling. We built a prototype in one weekend. The prototype gave better results than the commercial platform.

We continue to improve the pipeline. It is now fully automatic and the results are good.

The lesson is not "do not use commercial tools". Start with a commercial tool. Release your product MVP. Learn your true requirements. But move key architecture components like document processing to your own system as soon as possible.

Data processing is the base of all other functions. You need flexibility, cost control, and the ability to repair your own failures.

*Lessons 2 to 11 are possible only because we control the ETL pipeline.*

> Do you want a full article about the ETL pipeline, the architecture, the models, and the orchestration? [Tell us](/contact) and I will write it.

### Lesson 2: Owning the Pipeline Cut Our Costs 15 to 20x

![Cost — per-page pricing is arbitrary, your real cost is not](../../assets/13-things/fig-02-cost.svg)

Unstructured charged 20 USD for 1,000 pages at the start. The price then increased to 30 USD.

Per-page pricing does not reflect real cost. The true processing cost changes with the document. One page of simple text and one page of rotated engineering drawings are not equivalent. But the provider charges the same price for the two pages.

We operated our own pipeline for some weeks. We measured the cost with real customer documents. Our cost was 15 to 20 times less.

We did not use low quality models to get this result. We used good models. We operated the models on our own GPU infrastructure with our own routing.

For a small company, this is not just an improvement — it is months of extra runway.

### Lesson 3: Extraction Quality Sets the Maximum Performance of the System

![Extraction is the ceiling — no layer above can exceed the layer below](../../assets/13-things/fig-03-extraction-ceiling.svg)

You cannot correct bad extraction with a better retriever, a more intelligent agent, or a larger model.

A spec sheet has a key-value layout. If the extraction makes this layout into unstructured text, the data is lost. If the extraction ignores a rotated page, the data is lost. If a table loses its column alignment, the data is lost. No subsequent process can recover this data.

Technical documentation has these conditions frequently: rotated pages, dense tables, spec sheets with key-value layouts, scanned manuals that are 30 years old, and diagrams with important text in the image.

All our improvements in accuracy start with correct extraction.

### Lesson 4: Chunking is a Strategy, Not a Default Setting

We use hybrid chunking. This method divides the document by its structure and its hierarchy. It then merges the parts by token count.

Many developers use a recursive character splitter with a 512 token window. They do not change this setting again. Then they ask why the retrieval quality is low.

Your chunk boundaries control the possible results of the retriever. If a procedure is divided between two chunks, no retriever can give the full procedure to a technician.

### Lesson 5: Extract Taxonomy and Tags in the ETL Pipeline

![Taxonomy belongs in the ETL — tag at ingestion, filter at query time](../../assets/13-things/fig-05-taxonomy.svg)

We define a taxonomy during ingestion: manufacturer, model, and custom tags for each organization. The pipeline extracts the tags during preprocessing. We keep the tags as scalar filters with the vectors.

This looks like a small administrative task. It becomes a product function in Lesson 13. It is also a good example of a function that a closed commercial platform does not permit.

---

## Part 2. Retrieval

### Lesson 6: A Simple Top-K Semantic Search is Not Sufficient

![Naive top-K is not enough — similarity is not relevance](../../assets/13-things/fig-06-naive-topk.svg)

The first version of the agent at Opero did a simple top-K semantic retrieval. It found 10 to 20 documents by semantic similarity. It put the documents in the context. Then it generated an answer.

There was no reranking. There was no relevance filter. There was no procedure to find if the documents were best available for the user question.

We hoped that cosine similarity would find useful data. Then we gave the result to the user as an answer.

This method worked well enough for a demonstration. It failed often enough to be dangerous. This is the most dangerous failure condition in this field.

### Lesson 7: Add a Reranking Step

![Add reranking — retrieve wide, then judge what you retrieved](../../assets/13-things/fig-07-reranking.svg)

We added a reranking step. The system finds many candidate documents. A dedicated model then gives a relevance score to each candidate for that query.

The improvement was immediate and large. If you apply only one lesson from this list, apply this one.

We also use the relevance score in the user interface. If the best result has a low score, we tell the user. We do not give a confident answer from low quality context.

### Lesson 8: Rerank Scores Have No Absolute Scale — Pick a Model That Calibrates Them

![Standardise the score — a relevance number you cannot compare is not a number](../../assets/13-things/fig-08-standardised-scores.svg)

We started with Cohere Rerank. It operates correctly, but its relevance scores do not use an absolute scale.

So we had to tune the thresholds for each organization and each industry type. We had to decide which score was high, medium, or low. These thresholds were estimates. They also changed when the document collection changed.

We changed to zerank-2 from ZeroEntropy. This model gives standardized relevance scores.

This looks like a small change. It is not a small change. You can define a fixed relevance scale one time. You can then build product logic on this scale. The scale stays correct when your data changes. You do not have to keep a calibration procedure.

### Lesson 9: Use Hybrid Search If Your Users Type Serial Numbers

![Hybrid retrieval — two channels, one query, the technician types both](../../assets/13-things/fig-09-hybrid-search.svg)

We added BM25 keyword matching with the semantic search.

Engineers and technicians do not write complete questions. They type "E-047". They type a part number from a label. They type a serial number.

Vector search has low accuracy with these exact terms. This is not a defect. Embeddings find meaning, and an error code has no meaning.

Semantic search finds the answer for "why does the compressor short cycle".

BM25 finds the answer for "SCR-4471-B".

Users need the two methods, frequently in the same question.

### Lesson 10: Multiple Languages Are Usual

![Multiple languages are normal — not an edge case, the default case](../../assets/13-things/fig-10-multilingual.svg)

Most of our documentation is in English. But we also have documents in German, Danish, Swedish, and Chinese. European technical customers have documents in these languages.

Plan for multiple languages from the first day. It is difficult to add this function later.

---

## Part 3. Orchestration

### Lesson 11: Start with a Workflow. Change to an Agent Later.

![Start as a workflow — agency over an unreliable retriever is an expensive random walk](../../assets/13-things/fig-11-workflow-first.svg)

Our first version was not an agent. It was a DAG. We built it with LangGraph, and we still use LangGraph for all orchestration.

- **Node 1:** classify the input. Is it a question or a greeting? The system answers greetings directly and at low cost.
- **Node 2:** query the RAG system, format the result, and give it to the user.

That was the full system. This was the time of GPT-4o and Claude 3.5 Sonnet.

You can debug a deterministic workflow. You know which step failed. Agentic loops are more difficult to analyze. If you give autonomy to a loop that uses an unreliable retriever, the loop fails in unusual ways instead of predictable ways.

Repair retrieval first. Add agency second. Use this sequence.

### Lesson 12: Give the Agent Permission to Try Again

![Earn the retry — give the agent a score it can read and a line it can judge](../../assets/13-things/fig-12-earn-the-retry.svg)

We changed to a ReAct loop. The agent decides if the documents are sufficient. If they are not sufficient, the agent rephrases the query and does a new search.

This function operates only because of Lessons 7 and 8. The agent reads a standard relevance score. It finds that the results are not sufficient. It writes a new query. Then it does the search again.

Reranking gives the agent a signal. Standard scores give the agent a threshold. Agency without these two pieces is only an expensive random search.

We also added a pre-retrieval layer. This layer finds context quickly and sends it to the primary node. So the usual case is fast. The ReAct agent keeps its tools for a more complete search when necessary.

### Lesson 13: When the Agent Cannot Find an Answer, Give Control to the User

![Give control back — graceful degradation beats confident nonsense](../../assets/13-things/fig-13-give-control-back.svg)

This is my preferred part of the system. It is the result of Lesson 5.

If the first query gives results with a high relevance score, the agent responds immediately. If it does not, the agent requeries with a different angle.

But if the agent gets stuck in a loop and cannot find sufficient results, we do two things. We do not generate an incorrect answer. We also do not show only the message "no results found".

We show a filter interface. We build this interface from the taxonomy tags. The user selects a manufacturer, a model, or other data that the user knows. We then do the query again automatically with these filters.

The user knows data that the retriever does not know. Let the user nudge the agent.

Degrading gracefully is better than a confident incorrect answer.

---

## Part 4. Communications

### Bonus. MCP Makes Integration Simple

The primary loop is now reliable. So integration is configuration work instead of architecture work.

1. We added MCP servers for customer system integrations. The agent can now get live data, not only documentation, using [Nango](https://nango.dev/).

2. We added web search. The user can now include internet sources.

3. We also added voice, SMS, and email in one API/MCP layer using [Hail](https://hail.so).

This last item changes the function of the agent. The agent is no longer a chatbot that answers questions. It can call a technician. It can send a part number by SMS. It can send a report by email. The interface is no longer a text box. The interface is the system that the user has.

---

## The Primary Lesson

There is one pattern in all 13 items.

Every important improvement comes when we take control of a layer that we initially rented.

The ETL pipeline. The chunking. The taxonomy. The retrieval strategy. The orchestration logic. Each layer was initially an abstraction from a different company. Each layer becomes better when we can examine it and change it.

Start with the commercial platform. Release a product. Learn what is important. Do not build infrastructure for a product that has no users.

But you will get to the limits of that platform. Know which layer you will take control of first.

---

*I build [Opero](/), which supplies AI agents for technical field service, OEMs, HVAC, and manufacturing. I write about the problems we hit.*

*Which lesson do you want more information about? Readers ask about the ETL pipeline most frequently. [Tell us](/contact) and I will make it the next article.*

---

## Closing deals faster in industrial sales

Source: https://www.opero.pro/resources/2026-06-03-company-update

**Kind:** blog · **Read time:** 3 min · **Author:** Opero · **Tag:** Company Update · **Published:** 2026-06-03

We've partnered with **Nordic Electric** to put AI at the heart of industrial sales — instant technical answers, faster quotes, shorter cycles. Plus a new home on Microsoft Marketplace, and the latest from the product.

## Nordic Electric × Opero

In industrial sales, technical knowledge is the bottleneck. Buyers need answers, salespeople need them fast — and the engineers who hold them get pulled in every direction. Response times stretch from minutes to days. **Nordic Electric**, a Scandinavian supplier of electrical components and complete systems for renewable energy, the power grid, and industry, decided to fix that — not with more headcount, but by making knowledge itself instant.

Together, we're embedding AI directly into the commercial process. Their sales teams now ask technical questions and get **instant, accurate answers** drawn from their own documentation, specs, and past RFQs. We showed it live at the **EL & TEKNIK** conference — instant answers, component search, and drafted quotations, on stage.

> It is a true pleasure collaborating with Opero, who deliver AI solutions of the absolute highest quality. Their deep technical expertise combined with a fantastic understanding of our business makes a huge difference. They are innovative, professional, and always ready to go the extra mile to ensure the best result. We could not wish for a better partner to shape our digital future and look forward to our continued collaboration.
>
> **Jakob Toft** — CEO, Nordic Electric

## Now on Microsoft Marketplace

Opero is now available on the [Microsoft commercial marketplace](https://marketplace.microsoft.com/en-us/product/saas/operolabsaps1779889294390.opero-field-knowledge). For IT and procurement teams that standardize on Microsoft, that means a familiar buying process and vetted, enterprise-ready software — Opero sitting right next to the tools your business already trusts.

## Meanwhile, in the product

- **Two new integrations** — connect Attio and WooCommerce. Opero reads your deals, orders, and products, and acts on them, right from the chat.
- **Smarter auto-invoicing** — one consolidated draft per project in e-conomic.dk, with time, parts, and travel pulled in automatically. No duplicates, no manual entry.
- **One-click sign-in** — log in with Google or Microsoft, alongside the passwordless email link. No new password to manage.
- **Redesigned Cases & Documents** — cleaner, faster screens with at-a-glance stats and bulk controls, plus a smoother chat on mobile and desktop.

## Coming next: Opero that talks back

We're giving Opero full communication capabilities — so it can write and receive its own emails, SMS, and phone calls. Picture Opero following up with a customer, taking an inbound call, and handling the back-and-forth on its own. That's where we're headed next.

---

## More systems connected, invoices that write themselves

Source: https://www.opero.pro/resources/2026-06-02-product-update

**Kind:** blog · **Read time:** 5 min · **Author:** Opero · **Tag:** Product Update · **Published:** 2026-06-02

## Metrics

| Value | Label |
| --- | --- |
| 40+ | Updates shipped |
| 2 | New integrations |
| ✓ | All live now |

## TL;DR

- **Now on Microsoft Marketplace** — find and buy Opero directly on Microsoft's commercial marketplace.
- **Nordic Electric partnership** — we're putting AI at the heart of industrial sales, demoed live at the EL & TEKNIK conference.
- **Attio & WooCommerce** — connect your CRM and your store; the AI reads your deals, orders, and products, and acts on them.
- **Smarter auto-invoicing** — one consolidated draft per project, with time, parts, and travel pulled in automatically. No duplicates, no manual entry.
- **Sign in with Google or Microsoft** — one click, no new password.
- **Tag-aware AI search** — name a manufacturer or model and the AI pulls the right documents, or hands you a picker to narrow it down.
- **Redesigned everywhere** — Cases, Documents, and the chat home page, plus a smoother mobile experience.

Opero now plugs into more of your business, drafts smarter invoices end to end, and signs you in with one click. Plus two milestones worth celebrating. Here's everything new.

## Big news first

Two moments that say a lot about where Opero is headed.

### Opero is now a Microsoft Marketplace partner

You can now find and buy Opero directly on the [Microsoft commercial marketplace](https://marketplace.microsoft.com/en-us/product/saas/operolabsaps1779889294390.opero-field-knowledge). For IT and procurement teams that standardize on Microsoft, that means a familiar buying process and vetted, enterprise-ready software.

One more sign that Opero belongs in your stack — right next to the tools your business already trusts.

### Nordic Electric × Opero

In industrial sales, technical knowledge is the bottleneck. Together with **Nordic Electric** — a maker of custom electrical solutions for critical infrastructure — we're putting AI at the heart of the commercial process. Sales teams ask technical questions and get **instant, accurate answers** from their own documentation, specs, and past RFQs. We showed it live at the **EL & TEKNIK** conference: instant answers, component search, and drafted quotations.

> It is a true pleasure collaborating with Opero, who deliver AI solutions of the absolute highest quality. Their deep technical expertise combined with a fantastic understanding of our business makes a huge difference. They are innovative, professional, and always ready to go the extra mile to ensure the best result. We could not wish for a better partner to shape our digital future and look forward to our continued collaboration.
>
> **Jakob Toft** — CEO, Nordic Electric

## What's live now

Everything below is already in your account. No update needed — just log in.

### Connect Attio and WooCommerce

Opero now connects to **Attio** and **WooCommerce**. Ask the AI about a deal, a company, or a task in your CRM — and it can act, not just look. Connect your store and it reads live orders and products straight from the chat.

Better yet, a WooCommerce order tied to a project **attaches itself to the matching service report** automatically — so the parts a customer ordered show up on the invoice without anyone re-typing them.

### One project, one clean draft invoice

Auto-invoicing into **e-conomic.dk** grew up. Time from the service report and parts from your store now land on **a single consolidated draft per project** — no more duplicates when an order and a report arrive at different times.

Travel costs — flights, hotels, fuel, tolls, per-diems — are pulled in automatically. The right delivery date, currency, and references fill themselves in. Your finance team just reviews and sends.

### One click in with Google or Microsoft

Your team can now sign in with their existing **Google** or **Microsoft** account — alongside the passwordless email link you already have. No new password to create, remember, or reset. IT keeps control through the identity provider you already use.

### Tag-aware knowledge search

Name a manufacturer or model and the AI narrows straight to the right documents. When a search is too broad, it hands you a quick multi-select picker to refine — so you find the exact manual instead of wading through everything.

### One voicebot for answers and reports

The voice agent now answers questions and files a report in the same call, picks the right case mid-conversation, and reads numbers aloud in easy-to-follow groups. It works reliably across languages — even the less common ones.

### Cases, redesigned end to end

A cleaner list, detail, and form with drag-and-drop questions. New reference questions anchor each report to a known value — like a service address picked from your own list — and can hint which team member usually handles it.

### Documents page overhaul

At-a-glance stats, a faster toolbar, and a floating bar to change visibility on many documents at once. Share any document with a copy-link. And a new hidden mode lets the AI use a document as context while keeping it out of sight for external customers.

### A smoother chat, on phone and desktop

A redesigned chat home with quick-action cards and a personal greeting. On mobile, the composer, quick replies, and keyboard behave the way they should. Cited images now appear instantly, and a gentle warning tells you when a long chat is getting full.

## Three real scenarios

Here's what your team can already do today.

- **An RFQ lands at 9 AM** — a sales engineer asks Opero a technical question and gets an instant answer from the company's own specs and past quotes — plus a drafted quotation to send back. No waiting days for the one person who knows. The deal keeps moving.
- **A parts order lands mid-job** — a customer orders parts in your WooCommerce store while a job is open. The order attaches itself to the matching service report, and everything — time, parts, travel — merges into one draft invoice in e-conomic.dk. Finance reviews once and sends.
- **Your team lives in Attio** — ask Opero where a deal stands, log a note, and create a follow-up task — without leaving the chat. Your CRM stays up to date while you stay in the conversation. One place, less switching.

## Coming next: Opero that talks back

We're giving Opero full communication capabilities — so it can write and receive its own emails, SMS, and phone calls. Imagine Opero following up with a customer, taking an inbound call, and handling the back-and-forth on its own. That's where we're headed next.

---

## Opero × Nordic Electric: AI at the heart of industrial sales

Source: https://www.opero.pro/resources/nordic-electric

**Kind:** case-study · **Read time:** 4 min · **Author:** Opero · **Persona:** Sales · **Tag:** Partnership · **Industry:** MEP · **Published:** 2026-05-25

![Nordic Electric × Opero — commercial partnership]

> It is a true pleasure collaborating with Opero, who deliver AI solutions of the absolute highest quality. Their deep technical expertise combined with a fantastic understanding of our business makes a huge difference. They are innovative, professional, and always ready to go the extra mile to ensure the best result. We could not wish for a better partner to shape our digital future and look forward to our continued collaboration.
>
> **Jakob Toft** — CEO, Nordic Electric

We are proud to announce a commercial partnership with **Nordic Electric** — a company delivering custom electrical solutions for critical infrastructure, and one that is committed to becoming a truly AI-first organisation.

## The Challenge

In industrial sales, **technical knowledge is the bottleneck.** Buyers need answers. Salespeople need those answers fast. But the people who hold them — the sales engineers — are being pulled in every direction, fielding the same questions again and again instead of doing the work that actually needs them.

> Response times stretch from minutes to days. Engineers are trapped in repetitive Q&A. Sales cycles stall. And the knowledge that should be driving revenue sits locked inside individual heads rather than flowing across the entire organisation.

Nordic Electric recognised this pattern and chose to solve it the right way: not by adding headcount, but by making knowledge itself more accessible, instant, and intelligent.

## The Solution

Together, Opero and Nordic Electric are embedding AI directly into the commercial process. Sales teams can now ask technical questions and receive **instant, accurate answers** drawn from the company's own existing knowledge — product documentation, specifications, historical RFQs, and more.

This removes routine pressure from sales engineers and allows them to focus on the complex, strategic work that genuinely requires their expertise. The result is a faster sales cycle, shorter response times, and a more scalable organisation.

At this year's [**EL & TEKNIK**](https://elogteknikmessen.dk/) conference, Nordic Electric and Opero jointly showcased this capability live — demonstrating how AI transforms the RFP process by giving prospects instant access to technical documentation, component search, and quotation drafting.

- **Instant answers** — technical questions resolved in seconds, not days.
- **Component search** — find the right parts and pricing overviews immediately.
- **RFQ automation** — Opero analyses tender materials and drafts requests for quotation.
- **Living knowledge** — Opero continuously learns and improves over time.

## Why This Partnership Matters

At the heart of this collaboration is a shared belief: **AI should not be bolted on at the edges.** It should be embedded across the entire business — from operations to the commercial process — making every person in the organisation faster, smarter, and more capable.

Nordic Electric is on that journey. And Opero is proud to be the AI partner making it possible.

---

## Voicebots in the workshop: when speech beats screens

Source: https://www.opero.pro/resources/voicebot-workshop

**Kind:** blog · **Read time:** 8 min · **Persona:** Field eng. · **Tag:** Product · **Industry:** Cross-industry · **Published:** 2026-05-20

There's a reason field engineers don't want another tablet. Hands are oily, the noise floor is high, and the schematic is on the machine, not the screen. Voice is the right interface for the work. But the gap between "voice that works in a demo" and "voice that works in a workshop in November in Esbjerg" is the gap that decides whether technicians open the agent a second time. Here is what we learned shipping voice into the work.

## Why voice is not optional in this work

Field engineers don't have hands free for a tablet. Workshop ambient noise — pneumatic tools, line motors, conversation overlap — is loud enough that consumer voice assistants fall back to silence or to guesses. The schematic is taped to the side of the machine; the manual is in a cabinet across the floor. The technician's question lands while they are torquing a fitting or holding a probe to a sensor.

Voice is not a nice-to-have channel; it is the only channel where the engineer can keep working while the agent is answering. Voice and chat also answer from the same corpus and enforce the same ACLs — a technician on the phone gets the same answer a service manager gets in the portal. No separate knowledge silo.

Get this right and the agent earns the second use. Get it wrong — lag, mis-recognition, wrong answer delivered with confidence — and it becomes the tablet that nobody opens.

## Latency is the gate

Three seconds is the threshold. A voice agent that answers in 1.5 seconds reads as responsive; one that answers in 4 seconds reads as broken, even if the answer is correct. This is not a UX preference; it is the difference between an interface the technician returns to and one they abandon after the first shift.

The latency budget: speech-to-text around 300ms, retrieval from a well-tagged corpus another 200ms, LLM inference the dominant cost at 1–2 seconds, text-to-speech a final 300ms. Get all four right and you land under two seconds. Let any one drift and you are past three.

The decisions that move the budget are corpus tagging, model selection, and grounding strategy — not microphone hardware or network bandwidth, which are table stakes. Get those right and the agent feels like a colleague. Get them wrong and it feels like a help-desk queue.

## Multilingual code-switching is the real test

A Polish technician working a German press will start a sentence in Polish, drop in a German technical term mid-clause — "der Werkzeugträger ist locker" — and finish in Polish. Consumer voice assistants break here: they detect a language boundary at the sentence level, which is too late, and either mis-transcribe the term or switch the response language entirely.

The agent that handles this earns trust the first time. The mechanics are not magic: the speech-to-text layer has to detect language at the phoneme level, not the utterance level. The retrieval layer has to treat the technical term as a known noun anchored to the German parts catalogue, not as a transliteration problem. The response layer generates back in the technician's working language — Polish, in this case — with a citation pointing at the authoritative-language source document.

The same logic works in any multilingual dealer network. It is also where you find out fast whether the corpus is tagged by language and product line, or whether it is a single undifferentiated pile of PDFs.

## The failure mode that loses trust

Voice in the workshop fails most often not on recognition or latency, but on confidence. The scenario runs like this: the agent has recognised the machine model correctly, retrieved a plausible service bulletin, and generated an answer that sounds authoritative. Except the bulletin was issued for the previous model year. The technician torques to the wrong spec. The agent has lost trust — not by being wrong loudly, but by being confidently wrong on a specific.

This failure mode is upstream of voice. The corpus has to be tagged at ingest with effective date, supersession chain, applicable model range, and serial-number cutoff. A retrieval layer that cannot scope by model year will surface the wrong bulletin with full confidence, and voice makes the confidence audible in a way that text does not. Voice exposes the corpus-tagging discipline; it does not substitute for it.

If the corpus is not tagged this way before the voice rollout, the first weeks in production will teach you which documents are mis-tagged — useful feedback, but expensive feedback.

## Where to start next week

Pick one workshop, one product line, one language pair. Get a microphone array that handles the noise floor — this is real hardware money, not something you solve in software. Tag the corpus for that product line at ingest: effective date, supersession chain, model and serial range. Time the round-trip from the engineer's question to the spoken answer, and tune until it is under three seconds.

Then watch the second-use rate. The first use is curiosity; the second use is whether you have shipped a colleague or a help desk. Full product framing is at [Voice / Call Agent](/product/voice). The field-engineer persona and the numbers that service leadership reads weekly are at [Field engineers](/use-case/field). A live deployment at this scale — 60% drop in L2 escalation, 4.1× faster onboarding — is documented at [Nize Equipment](/case-study/nize-equipment).

---

## The 2026 RFP automation playbook for industrial OEMs

Source: https://www.opero.pro/resources/rfp-playbook

**Kind:** guide · **Read time:** 12 min · **Persona:** Sales · **Tag:** Operations · **Industry:** Cross-industry · **Published:** 2026-05-18

Most industrial RFPs are won or lost on the same 20% of the document, and the other 80% is boilerplate that a junior could write in their sleep. The point of automation isn't to fire the bid team — it's to give them the time to fight on the part that matters.

## The 80/20 split nobody talks about

Pull apart any 200-page RFP response and you'll find the same shape. Eighty percent is compliance language, company history, certifications, standard product spec, references — material that's been answered the same way for a decade. The remaining twenty percent is the bid: pricing, scope carve-outs, delivery commitments, the carefully worded clauses that decide whether you're profitable or underwater.

Bid managers know this. They also know they spend roughly the opposite ratio of their time: most of the week chasing the boilerplate down from product, legal and quality, and the last frantic afternoon on the part that actually wins or loses the deal. Automating that ratio is the whole game.

## What to automate first

Three things, in this order. First, compliance matrix extraction — point the agent at the incoming bid PDF and have it produce a structured matrix of every "shall," "must" and "is required to" with a section reference and a draft response sourced from past wins. This alone collapses the first two days of any RFP.

Second, tone-matching against your past winning responses. Generic LLM output reads like a consulting deck; your real bids read like your company. Train the retrieval against the corpus of *won* RFPs, not every RFP, and the drafts come out sounding like you.

Third, pricing guard-rails per product line. The agent should never quote a price, but it should flag when the scope it's drafting against implies a line item outside the standard margin band — so the bid manager sees it before the customer does.

## Human-in-the-loop, always

We do not auto-submit. Ever. The pattern that works is: agent drafts, bid manager reviews section-by-section in a side-by-side view with the source citations, accepts or rewrites, and signs the final PDF. The agent owns the keystrokes; the human owns the commitment.

This isn't a hedge — it's how trust accrues. The first few bids, the manager rewrites a lot. By bid five they're accepting most sections untouched and spending their week on pricing strategy instead of paragraph wrangling. That's the win.

## A typical first-bid result

A typical pilot looks like this. The first response that used to take three weeks now goes out in hours, often the same day the RFP lands. Drafts come back at roughly 5× the throughput of the manual baseline, and time-to-first-draft drops about 70% (illustrative, typical pilot — your mileage will depend on corpus quality).

The number that actually changes the business isn't throughput, though. It's *coverage*. Teams who used to no-bid two out of three opportunities because they didn't have the hours now respond to everything that fits the ICP, and let the customer's shortlist do the qualifying. That's where the new revenue comes from — not faster bids on the deals you were already chasing, but bids on the deals you used to skip.

## Where to start next week

Pick one product line. Gather the last twelve months of won and lost RFPs into a single corpus, tagged by outcome. Have your bid manager spend two hours marking up a recent response with what they'd have written differently. That document — corpus plus markup — is enough to pilot against the next live RFP. Don't wait for the perfect data lake; the first bid is your first dataset.

---

## How EU-hosted AI changes procurement conversations

Source: https://www.opero.pro/resources/eu-hosted

**Kind:** blog · **Read time:** 5 min · **Persona:** C-suite · **Tag:** Compliance · **Industry:** Cross-industry · **Published:** 2026-05-15

Sovereign and EU-hosted deployments shorten the procurement conversation by removing the data-residency objection before it comes up. When a security questionnaire opens with a question you have already answered, the procurement team's read on you shifts from "another US-hosted AI vendor" to "a vendor who understood the question." Three procurement objections surface in order: data residency, training-pool exposure, and audit coverage. Each has a short answer if the vendor has done the infrastructure work.

## What "EU-hosted" actually has to mean

"We have a data centre in Frankfurt" is not a sufficient answer, and a procurement team that has run this process before knows it. The questions arrive in a specific order:

1. **Where does customer data rest?** Opero runs on EU infrastructure under GDPR jurisdiction. Single-country pinning is configurable at contract time and enforced at the infrastructure layer.

2. **Where does it transit?** Customer data does not leave EU territory in transit during retrieval, inference, or action execution. No traffic routes through US-based infrastructure.

3. **Who has read access?** Retrieval is filtered by user permissions; each customer runs in a dedicated tenant with no cross-customer pooling of corpus, conversation history, or metadata.

4. **What happens if a non-EU authority subpoenas your infrastructure provider?** The infrastructure is in EU jurisdiction; sovereign on-prem deployment is available for stricter mandates.

Most "EU-hosted" claims survive question 1 and break on question 2.

## The training-pool objection

"Do you train on our data?" Most LLM vendors' honest answer is: we say no in the contract, but the operational architecture would permit it. The architecture that closes this question has a different shape.

Opero does not train on customer data. There is no training pool. When a technician asks a question, the [Knowledge Agent](/product/knowledge) retrieves the relevant chunks from your corpus and passes them to the model as context at retrieval time. Your data is in the prompt, not in the model weights. A fragment of one customer's service manual cannot surface in another customer's answer because the retrieval draws from a per-tenant corpus — there is no shared index across customers.

The procurement team can verify this by asking two questions: does your inference pipeline write anything back to a training data store, and what is your subprocessor's model-training policy for data processed via API? Both questions have short answers when the architecture is right. When they don't, the conversation gets long, and long procurement conversations about training-pool exposure rarely close well.

## Audit log as procurement currency

"Show me what your system did six weeks ago for this user on this document." That question ends procurement conversations in one of two ways: the vendor opens a log and walks through the answer, or the vendor explains why they can't do that. The first vendor gets the contract.

Every retrieval, every cited source, every outbound action — PO draft, ticket update, work-order note — is logged with the calling user, timestamp and model version. The log is append-only and scoped to your tenant. When a procurement auditor asks what happened on a specific date, the log shows which user triggered the query, which documents were retrieved, what version of the model responded, and what action — if any — was written back to the ERP.

The log is replayable. You can reconstruct the exact retrieval that generated a given answer, which makes it usable in a formal audit rather than an informal retrospective. "We have logs" is a much shorter conversation than "let me check."

## Where to start next week

For procurement teams reviewing AI vendors: ask the egress question and the training-pool architecture question — in that order. The answers separate vendors faster than any security questionnaire. For vendors preparing for the security review: lead with the audit-log demo. Data residency is table stakes in the EU market; audit replay closes the meeting. Full operational-trust framing is in [Operational trust](/resources/trust). Hosting-region details and the subprocessor posture are on the [Trust page](/trust).

---

## Why generic LLMs miss the mark in manufacturing service

Source: https://www.opero.pro/resources/generic-vs-vertical

**Kind:** blog · **Read time:** 7 min · **Persona:** C-suite · **Tag:** Thought leadership · **Industry:** Cross-industry · **Published:** 2026-05-13

Generic LLMs — Copilot, ChatGPT, the assistant baked into your CRM — are remarkable general-purpose tools, and they hit a wall the moment a technician asks them about a real machine. The failure isn't in the model. It's in everything around the model that nobody built.

## The failure modes are domain-specific

Parts SKU confusion is the first thing that breaks. A bearing carries a different part number across three model years and two regional catalogues, and the generic model will confidently pick one — usually the one most represented in its training data, which is rarely the one in front of the technician. There is no clever prompt that fixes this; the model doesn't know what it doesn't know.

Then come the service bulletins. OEMs issue them, supersede them, withdraw them. Generic models trained on a snapshot don't model supersession at all and will happily quote a bulletin that was pulled eighteen months ago. Then there's the multilingual question: a Polish technician asks in Polish, the manual is in German, the answer comes back in English. And then EU compliance — data residency, audit logs, retention — which the generic procurement conversation has barely begun to acknowledge.

## A concrete example: "E-12"

Across one customer's product lines, "E-12" means three different things. On the older diesel platform it's a fault code for low oil pressure. On the newer electric platform it's a battery management warning. In the parts catalogue it's the SKU prefix for a family of harnesses. Ask a generic LLM "what does E-12 mean on our machines?" and you'll get a confident, plausible, wrong answer.

A vertical model with the corpus tagged by product family, model year and document type doesn't have to guess. It returns the right answer with the right citation because the retrieval narrows on metadata before the language model ever sees the candidates. That's not a smarter model; that's a system designed for the problem.

## "Bring your own data" isn't enough

The standard answer from the generic vendors is: upload your PDFs, we'll RAG it. That misses what the work actually requires. The schema — parts, machines, bulletins, work orders, customers — isn't in the PDFs; it has to be modelled. The citation pattern that earns technician trust isn't a default; it has to be built. Parts-aware reasoning across cross-references and supersessions isn't a prompt; it's a retrieval layer. And the voice channel — the one technicians actually use because their hands are occupied — isn't a feature you bolt on after.

A general-purpose model dropped into a technical-team workflow is a brilliant graduate on day one of the job: fluent, confident, and wrong about specifics in ways that nobody senior has time to keep correcting. The fix isn't a better graduate. It's the apprenticeship — the schema, the corpus, the citations, the channel — and that's what has to be built, not configured.

## Where this leaves industrial buyers

The companies getting real value out of AI in service operations in 2026 are not the ones who picked the biggest generic model. They're the ones who picked a system designed around their corpus, their parts, their bulletins, their languages and their compliance posture, and accepted that the model is one component of five rather than the whole product.

That's the bet Opero is built on, and it's why we don't compete on benchmarks — we compete on whether the technician opens the app the second time. Serving technical teams is a vertical problem. Treat it like one.

---

## Building a knowledge agent your technicians actually trust

Source: https://www.opero.pro/resources/trust

**Kind:** guide · **Read time:** 9 min · **Persona:** Service mgr · **Tag:** Operations · **Industry:** Cross-industry · **Published:** 2026-05-11

Technicians don't distrust AI because it's AI. They distrust it because the first three answers were almost right, and "almost right" on a 40-ton press is how people get hurt. Trust is operational — you build it in the corpus, the citations and the audit log, not in the marketing copy.

## Document-level citations, on purpose

Every answer the agent gives points back to the source PDF and page — not a highlighted line, not a paraphrase. That's a deliberate choice. When a technician opens the citation they see the answer in *context*: the warning above it, the torque spec below it, the revision date in the header. A line-level citation hides all of that and quietly trains the reviewer to skip the source.

Document-level surfacing also keeps the agent honest. If the model paraphrased something the page doesn't say, the technician sees the page and notices. That gap is the most valuable feedback signal in the whole system.

## Revision awareness is half the work

Service bulletins get superseded. Manuals get reissued. The most common trust failure we see in the wild isn't hallucination — it's a confident answer cited to a bulletin that was withdrawn eighteen months ago. Generic RAG pipelines don't catch this because they don't model "supersedes" as a relationship; they just index every PDF in the share.

Getting this right is unglamorous. Someone has to tag the corpus with effective dates, supersession chains, applicable model and serial ranges. There is no shortcut. But once it's tagged, the agent will refuse to cite a withdrawn document, and that single behaviour earns more trust than any UI polish.

## Role and document permissions

Sales does not need to see engineering drawings. Field technicians do not need to see commercial pricing. The same agent serves both, but the retrieval is filtered by the user's role before the model ever sees the candidate documents — not after. Filtering post-generation is how leaks happen.

Document-level ACLs sit alongside the revision tags in the same metadata layer, which is also where customer-confidential material gets walled off per tenant. Get the permissions schema right at week one; retrofitting it later is painful.

## Audit logs for every retrieval and every action

Every question, every retrieved chunk, every cited source, every outbound action — PO draft, ticket update, work-order note — is logged with the user, timestamp and model version. This matters for two reasons. One: when a technician disputes an answer six weeks later, you can replay the exact retrieval and see whether the corpus has drifted. Two: regulated customers will ask for this, and "we have logs" is a much shorter conversation than "let me check."

## The feedback widget is the eval set

Every answer carries a thumbs-up / thumbs-down with a one-line "why." Every thumbs-down becomes a row in the evaluation set, reviewed weekly, and the failure mode gets traced to either the corpus (missing or mis-tagged doc), the retrieval (wrong chunk surfaced) or the generation (model paraphrased badly). Each layer has a different fix.

The teams that get the most out of their agent are the ones who treat the eval set as a living document, not a launch artefact. Three months in, your eval set is more valuable than the model.

## Trust is operational, not a feature flag

There is no "trustworthy AI" toggle. There's a corpus that's tagged correctly, a citation pattern that puts the source in front of the reviewer, a permissions model that doesn't leak, an audit log that lets you replay any answer, and a feedback loop that closes. Do those five things and the technicians start opening the agent before they pick up the phone. Skip any one and they won't, no matter how good the model gets.

---

## Your customers served, your invoices written, zero friction

Source: https://www.opero.pro/resources/2026-04-10-product-update

**Kind:** blog · **Read time:** 5 min · **Author:** Opero · **Tag:** Product Update · **Published:** 2026-04-10

## Metrics

| Value | Label |
| --- | --- |
| 7 | Updates shipped |
| 3 | Major features |
| ✓ | All live now |

## TL;DR

- **White-label customer portal** — your customers log in at help.mycompany.com with your branding, troubleshoot with AI, and order spare parts. No support call needed.
- **Auto-invoicing via e-conomic.dk** — technician closes a job → draft invoice with time, parts, travel, and service report PDF. Zero manual entry.
- **Custom AI persona** — set tone, terminology, and industry context in Settings. E.g. make it speak like a senior field engineer using ISO standards.
- **Smarter voice AI** — waits when someone pauses to think or look something up. Works in any language.
- **Documents in chat** — the AI reads, fills, and writes documents. E.g. drop a price list → get a cost comparison, or ask it to write a procurement offer → download as PDF/Word or email it straight to a supplier.
- **Email & SMS from chat** — send a procurement offer to a supplier or text a customer a job update, with attachments. One conversation.
- **Passwordless login** — enter your email, get a link in your mailbox, click it and you're in. No passwords to manage.

Opero now runs under your brand, writes invoices automatically, and lets you control exactly how the AI works for your team. Here's everything new.

## What's live now

Everything below is already in your account. No update needed — just log in.

### Let your customers help themselves

Your customers can now access Opero through **their own portal** — at mycompany.opero.pro or a fully custom URL like help.mycompany.com. They log in, search your documentation, troubleshoot problems, and order spare parts — all without calling your support line.

Your logo, your name, your look. For your customers, it just feels like part of your service. For you, it means **fewer support calls**, **faster answers** for your clients, and a forward-looking service experience that helps win more deals.

### Closed job? Draft invoice — done

Opero now connects to **e-conomic.dk** (by Visma). When a job is closed and approved in your CRM, ERP, or FSM system, a **draft invoice is created automatically** in e-conomic.dk — complete with customer details, time entries, travel, parts, and a PDF of the service report attached.

No more manual data entry between systems. Your finance team just reviews and sends — that's it.

### Make the AI work the way you do

Admins can now **customize how the AI assistant behaves** — directly from Settings. Define its persona, set the industry context, write response guidelines, and control formatting preferences.

Whether you need the assistant to speak like a senior engineer, follow ISO terminology, or always respond in a specific format — it's all configurable. No code, no developers needed. Your AI, your rules.

### Smarter voice AI conversations

The voicebot now handles pauses and silence intelligently — no more cutting in while someone is thinking or looking something up. Whether your team speaks Swedish, Danish, English, or any other language, conversations feel more natural.

### Create, edit & read documents from chat

Upload Word, Excel, or PDF files and the AI reads, understands, and works with them. A few examples:

- **Service manager** — upload a service report template, tell the AI the job details, download the filled report
- **Procurement** — drop a supplier price list in Excel, ask for a cost comparison or a formatted offer
- **Finance** — upload a timesheet, get a summary of hours and costs per project, ready for invoicing

### Send emails & SMS directly from Opero

Ask the AI to send an email or SMS — without leaving the chat. Send yourself a job summary, forward a procurement offer to a supplier, share a document with a colleague, or text a customer with an update. Attach files, hit send — done.

### Passwordless login

Your team and your customers now log in with just their email — a link is sent automatically, one click and you're in. No passwords to forget or manage.

## Three real scenarios

Here's what your team can already do today.

**Your customer has a problem at 2 AM.** They open your Opero page, and describe the issue. The AI pulls the answer from your service manuals, walks them through the fix, and generates a spare parts list to order from your catalog. Your support team sleeps through the night.

**A technician drives home from a job.** The service report was approved before they left the site. By the time they're back, there's a draft invoice in e-conomic.dk — time, parts, travel, PDF attached. Finance reviews and hits send. Less time spent, less human error.

**A customer asks for a quote before lunch.** Your sales rep drops the price list into Opero, tells the AI what to include, and downloads a finished procurement offer as PDF. One more message: "email this to the customer." Sent — before the coffee gets cold.

## Coming next: Procurement AI

We're building a procurement solution that helps manufacturers respond to tenders with offers based on their product catalog in Opero. Upload a tender, match it against your catalog, and write a ready-to-send offer — all from Opero.

---

## More intelligence, less admin, every shift

Source: https://www.opero.pro/resources/2026-03-18-product-update

**Kind:** blog · **Read time:** 4 min · **Author:** Opero · **Tag:** Product Update · **Published:** 2026-03-18

## Metrics

| Value | Label |
| --- | --- |
| 11 | Updates shipped |
| 6 | In the works |
| Q2 | Next big release |

## TL;DR

- The AI now thinks deeper before answering — and can search the internet when needed
- Upload videos and photos in chat or attach them to service reports
- Every completed job automatically becomes searchable knowledge
- Documents are automatically tagged by manufacturer and model for faster, more accurate answers
- Order spare parts directly from chat — get a full parts list and send it where it needs to go
- Reports are sortable and shareable in one click — editing coming in Q2
- SMS and email reminders are live, ERP/CRM/FSM integrations expanding in Q2/Q3
- Full service reports via chat — ideal for jobs that span days, weeks, or months
- Mobile redesign underway — built for the field

Here's everything we've built for you since January — and what's coming next. Designed for the teams who keep operations running.

## What's live now

Everything below is already in your account. No update needed — just log in.

**The AI now thinks deeper before it answers.** We've upgraded how the AI works behind the scenes. Instead of simply looking up the closest document, Opero now reasons through the problem step by step before giving you an answer. It can also search the internet when needed — so if the answer isn't in your documents, Opero can still help. For your team, this means fewer dead ends when troubleshooting — and more accurate, relevant answers that match the actual situation.

- **Order spare parts from chat** — Ask Opero for the spare parts list for any machine or component. You'll get a full list with quantities, part numbers, and descriptions — then send it directly to the manufacturer, your procurement team, or a colleague.
- **Visual troubleshooting** — Take a photo or video of a component or fault and ask Opero to help diagnose it. The AI analyses what it sees alongside your documentation to give a relevant answer.
- **Automatic answers on ERP/CRM/FSM tickets** — When a ticket opens in your system (Business Central, SAP, Dynamics, or similar), Opero finds relevant knowledge and posts a suggested resolution as a comment — automatically.
- **Service reports via chat** — Your team can now complete full service reports directly through chat — with the AI guiding the conversation, and the ability to upload photos, videos, and documents along the way. Come back to it any time. Perfect for jobs that span days, weeks, or even months.
- **Every job builds your knowledge** — When a voice call ends, the transcript is automatically added to your knowledge base. No uploads, no extra steps. Your team's experience becomes searchable with every completed job.
- **Video & photo uploads in chat** — Your team can now share videos and photos directly in the chat — to ask questions, provide context, or document evidence. They can also be attached to service reports.
- **Past jobs help future jobs** — Service reports from previous jobs now appear as sources when your team asks questions. So what was learned last week can help someone in the field today.
- **Find reports by job or work order** — Search and sort reports by work order or reference number. No more scrolling through long lists — find what you need instantly.
- **Share reports to the knowledge base** — Any report can be added to the knowledge base with a single click — making it searchable by your entire team, with no extra steps.
- **Automatic document tagging** — Documents are now automatically tagged by manufacturer and model to improve accuracy and speed. You can also add your own tags manually for even more precise results.

**Bug fix.** We fixed an occasional issue where the AI could lose track of earlier messages in a conversation. The chat now reliably remembers everything discussed, even in longer exchanges.

## Three real scenarios

Here's what your team can already do today.

- **A technician hits an unfamiliar fault** — They search the chat. Opero finds a matching answer from a previous job report — documented, transcribed, and added automatically. Problem solved without calling anyone.
- **A manager reviews the day's work** — Opens the reports — all generated automatically from voice calls. Reviews them, shares two to the knowledge base. Done in 10 minutes.
- **A new hire in their first week** — Asks Opero a question. Gets an answer drawn from training materials and three similar past jobs. Learns faster without needing to interrupt senior colleagues.

## What we're working on

These are in active development. Most are planned for Q2 2026.

- **Predictive maintenance calendar** — Opero reads maintenance schedules from your manuals (e.g. "replace part X every 10,000 hours") and combines that with data from service reports and job history. The result: a clear view of when each machine needs its next service. Your team can plan ahead, reduce unplanned downtime, improve first-time fix rates, and take on more business. **Interested in being an early user?** We'd love to hear from you.
- **Pre-filled report templates** — Define the questions you want answered in advance. Reports come back structured and ready to review — no more starting from a blank page.
- **Simplified mobile experience** — A cleaner mobile design — fewer taps, no repeated logins. Built for someone in the field with 90 seconds between jobs.
- **Automatic capture when a job closes** — When a technician closes a work order in your ERP/CRM/FSM, Opero starts the knowledge capture automatically. No manual trigger needed.
- **Editable reports** — Review and edit auto-generated reports before sharing — add notes, correct details, and approve content in one step.
- **Advanced document tagging** — Automatic tagging by manufacturer and model is already live. Next: tag by machine type, category, or site — so answers are scoped to exactly the right equipment.

> I've never seen this before — just getting a technician to fully document their work is already a challenge. Capturing that per job and being able to use it for troubleshooting later — super valuable.
>
> **Sune Lysgaard-Hansen** — Service Development Manager, SH Group

---

## How Nize Equipment cut L2 escalations by 60% in 90 days

Source: https://www.opero.pro/resources/nize-equipment

**Kind:** case-study · **Read time:** 6 min · **Persona:** Service mgr · **Tag:** Service · **Industry:** Production · **Published:** 2026-01-02

A small service team supports customers across Scandinavia. We helped them turn 18 years of service docs, machine manuals and tribal knowledge into an agent every technician now opens before picking up the phone. The numbers: 60% drop in L2 escalations in 90 days, 4.1× faster onboarding, 92% weekly active technicians.

[Read the full case study →](/case-study/nize-equipment)

# Compare

---

## Opero vs. ChatGPT / OpenAI

Source: https://www.opero.pro/compare/chatgpt

Choose ChatGPT for broad individual work. Choose Opero when a recurring industrial process needs configured documents, systems, permissions, and support.

## Opero vs. ChatGPT / OpenAI

| Dimension | Opero (AI partner for industry and manufacturing) | ChatGPT / OpenAI (General AI Assistant) |
| --- | --- | --- |
| **Best fit** | | |
| Target user | Service engineers, field teams and the customers they support | General users & developers |
| Platform role | Technical knowledge and workflow automation, connected to your business systems (Customer-facing, Internal, Cross-system) | Broad assistant and API platform; customer-facing or specialised applications require configuration or development (Flexible platform) |
| **Technical knowledge** | | |
| Technical documentation *(unique)* | Configured for large collections of manuals, drawings, procedures, videos, and service records, with sources shown beside answers (Document scope, Source-cited) | Supports uploaded files, connected sources, projects, and custom configurations; scope depends on the chosen product and setup (Broad use cases, Setup dependent) |
| Technical doc specialization | Implementation includes organising OEM manuals, drawings, service procedures, revisions, and machine-specific information | Projects, connected sources, custom GPTs, and APIs can support specialised work when the organisation configures them (Flexible setup) |
| Knowledge capture & update | Interview staff, generate service reports, and keep documentation up to date with live conversations — via voice or chat (Voice capture, Chat capture, Auto-update KB) | Can draft and structure supplied information; the capture process must be configured |
| **Voice Interface** | | |
| AI voice agent | Dedicated phone number backed by an AI agent — technicians call to troubleshoot issues, file reports, and query the knowledge base hands-free (Phone number, Troubleshooting, Reporting, KB queries) | Voice mode is available for general conversations; industrial reporting requires a configured application or workflow |
| **Workflow Integrations** | | |
| Connected workflows | Connects directly to Microsoft Dynamics, Zoho, e-conomic and more — auto-create invoices after a job, draft customer responses, trigger follow-up actions (Dynamics, Zoho, e-conomic, Auto-invoicing) | Can support specific workflows through GPTs, agents, and APIs; the organisation owns or commissions the implementation (Implementation needed) |
| Named integrations | SAP · Microsoft 365 · Dynamics · Zoho · e-conomic · CMMS platforms · custom ERP/DMS — built to connect, not replace | APIs and connected applications support broad integration; specialised ERP work depends on the chosen implementation |
| RFP / RFQ automation *(unique)* | Reads RFP and RFQ documents, drafts sourced answers, and returns the response for bid-manager review (Doc-grounded, Auto-draft) | Can analyse files and draft responses; a structured tender workflow and source controls require configuration (Configuration needed) |
| **Customer-Facing Capabilities** | | |
| Customer portal *(unique)* | Deploy a branded customer-facing AI portal scoped to curated documents — customers get self-serve support without accessing internal knowledge (White-label, Scoped access, Branded subdomain) | Possible through APIs and applications; branding, document scope, identity, and access controls must be implemented |
| Spare parts ordering *(unique)* | Customer describes a maintenance need → Opero surfaces the correct parts list → customer generates a formal order directly to the manufacturer | Can assist through a configured application; catalogue matching, permissions, and ordering must be built |
| **Implementation** | | |
| Time-to-first-value | A defined first process is typically prepared for production within six weeks, including guided setup and testing (< 6 weeks) | General use can begin quickly; a controlled industrial application depends on configuration, integration, and testing (Scope dependent) |
| **Business data controls** | | |
| Data used to train public models | Customer data is not used to train models; provider and processing terms are documented for the deployment | OpenAI states that Business, Enterprise, Edu, Healthcare, and API data is not used for training by default (Plan-specific controls) |
| Access and isolation approach | Separate customer environments, permission checks before model use, EU hosting by default, and private deployment options | Business products provide encryption and workspace controls; retention and residency options vary by product (Product-dependent) |
| Data residency & deployment | EU-hosted by default, with private and on-premises options available by agreement (EU, On-premise option, Contract-defined) | Regional storage and processing options are available for eligible business and API customers |

ChatGPT works well for research, drafting, analysis and other general office tasks. It can also work with files and custom instructions. Using it as a technical service tool, however, requires your team to connect the relevant documents and systems, manage permissions, and decide how answers should cite controlled sources.

Opero includes that implementation work. We organise manuals, service records and parts data, connect the systems your team uses, and configure what the assistant may answer or do. The same approved knowledge can support chat, phone calls and workflows.

## When ChatGPT is the better choice

Choose ChatGPT when people need a broad assistant for writing, research, coding or ad hoc analysis. It covers more general tasks and is familiar to many users. Some teams use ChatGPT for individual knowledge work and Opero for questions and processes that depend on controlled technical data.

## Related pages

- [Knowledge Agent](/product/knowledge) — how Opero finds and cites information from your documents.
- [Trust](/trust) — hosting, data handling, permissions and audit logs.
- [Why generic LLMs miss the mark](/resources/generic-vs-vertical) — a longer explanation of the difference.

---

## Opero vs. Claude (Anthropic)

Source: https://www.opero.pro/compare/claude

Choose Claude for broad writing, coding, and analysis. Choose Opero when technical knowledge and connected industrial processes must be configured and maintained.

Claude is well suited to writing, coding and analysing material supplied by the user. Its large context window is useful when someone wants to work through a substantial document or set of files in one conversation.

Technical service teams usually need more than a long conversation. They need an assistant that already has access to the correct manual revision, respects document permissions, cites the relevant page and can use the same information in a phone call or workflow. Opero sets up and maintains those connections for the customer.

## When Claude is the better choice

Choose Claude when the work is general writing, coding or analysis and the user can provide the necessary context. Opero is intended for recurring operational work that depends on a maintained collection of technical documents and business data. A company can use both for different tasks.

## Related pages

- [Knowledge Agent](/product/knowledge) — cited answers and controlled access to technical documents.
- [Trust](/trust) — where customer data is stored and how it is handled.
- [Why generic LLMs miss the mark](/resources/generic-vs-vertical) — a longer comparison of general and specialised assistants.

---

## Opero vs. Microsoft Copilot

Source: https://www.opero.pro/compare/copilot

Choose Copilot for work centred on Microsoft 365. Choose Opero when technical knowledge must drive work across service, ERP, parts, and customer systems.

## Opero vs. Microsoft Copilot

| Dimension | Opero (AI partner for industry and manufacturing) | Microsoft Copilot (General Enterprise AI) |
| --- | --- | --- |
| **Best fit** | | |
| Target user | Service engineers, field teams and the customers they support | Office & knowledge workers |
| Platform role | Technical knowledge and workflow automation, connected to your business systems (Customer-facing, Internal, Cross-system) | Broad assistant embedded in Microsoft 365, with extensions available through Microsoft tools (Microsoft 365, Employee productivity) |
| **Technical knowledge** | | |
| Technical documentation *(unique)* | Configured for large collections of manuals, drawings, procedures, videos, and service records, with sources shown beside answers (Document scope, Source-cited) | Uses Microsoft 365 content the user may access and can include citations to information used for an answer (Microsoft Graph, Citations supported) |
| Technical doc specialization | Implementation includes organising OEM manuals, drawings, service procedures, revisions, and machine-specific information | Works with Microsoft 365 and connected organisational data; specialised technical structure must be configured (Configuration needed) |
| Knowledge capture & update | Interview staff, generate service reports, and keep documentation up to date with live conversations — via voice or chat (Voice capture, Chat capture, Auto-update KB) | Can draft and summarise in Microsoft 365; the capture process must be configured |
| **Voice Interface** | | |
| AI voice agent | Dedicated phone number backed by an AI agent — technicians call to troubleshoot issues, file reports, and query the knowledge base hands-free (Phone number, Troubleshooting, Reporting, KB queries) | Voice is available in Microsoft experiences; a dedicated technical service line requires additional setup |
| **Workflow Integrations** | | |
| Connected workflows | Connects directly to Microsoft Dynamics, Zoho, e-conomic and more — auto-create invoices after a job, draft customer responses, trigger follow-up actions (Dynamics, Zoho, e-conomic, Auto-invoicing) | Strong fit for work in Microsoft 365; industrial processes require connectors, agents, or additional configuration (Microsoft 365 focus) |
| Named integrations | SAP · Microsoft 365 · Dynamics · Zoho · e-conomic · CMMS platforms · custom ERP/DMS — built to connect, not replace | Deep Microsoft 365 and Azure integration, with options for external data through connectors and extensions (Microsoft ecosystem) |
| RFP / RFQ automation *(unique)* | Reads RFP and RFQ documents, drafts sourced answers, and returns the response for bid-manager review (Doc-grounded, Auto-draft) | Can draft in Word and use Microsoft 365 content; a tender-specific review process must be configured (Configuration needed) |
| **Customer-Facing Capabilities** | | |
| Customer portal *(unique)* | Deploy a branded customer-facing AI portal scoped to curated documents — customers get self-serve support without accessing internal knowledge (White-label, Scoped access, Branded subdomain) | Primarily an employee experience; customer-facing delivery requires another Microsoft application or custom solution |
| Spare parts ordering *(unique)* | Customer describes a maintenance need → Opero surfaces the correct parts list → customer generates a formal order directly to the manufacturer | Can assist with information and Microsoft workflows; catalogue matching and ordering require connected systems |
| **Implementation** | | |
| Time-to-first-value | A defined first process is typically prepared for production within six weeks, including guided setup and testing (< 6 weeks) | Microsoft 365 productivity can begin quickly; specialised processes depend on data readiness and implementation scope (Scope dependent) |
| **Business data controls** | | |
| Data used to train public models | Customer data is not used to train models; provider and processing terms are documented for the deployment | Microsoft states that Microsoft 365 Copilot interaction data is not used to train foundation models (Business protection) |
| Access and isolation approach | Separate customer environments, permission checks before model use, EU hosting by default, and private deployment options | Uses Microsoft 365 identity, permissions, sensitivity labels, encryption, and compliance controls (Microsoft controls) |
| Data residency & deployment | EU-hosted by default, with private and on-premises options available by agreement (EU, On-premise option, Contract-defined) | EU Data Boundary and regional options are available for eligible Microsoft products and customers |

Microsoft Copilot is designed for work in Microsoft 365. It helps users draft documents, analyse spreadsheets, prepare meetings and find information in the Microsoft Graph.

Opero addresses a different set of tasks. It works with sources such as OEM manuals, service records, parts catalogues, ERP data and CMMS records. Teams can use that information through chat or voice and can build workflows that update the systems involved in the process.

## When Microsoft Copilot is the better choice

Choose Copilot when most of the work happens in Word, Excel, Outlook, Teams and other Microsoft products. Choose Opero when the work begins with technical documentation or needs to continue into service and operational systems. Companies that need both commonly run them alongside each other.

## Related pages

- [AI Workflows](/product/workflows) — examples of processes that span ERP, CMMS and other systems.
- [Trust](/trust) — hosting, permissions and audit controls.
- [Why generic LLMs miss the mark](/resources/generic-vs-vertical) — more detail on general-purpose and domain-specific tools.

---

## Opero vs. Dust

Source: https://www.opero.pro/compare/dust

Choose Dust when an internal team wants to build many agents. Choose Opero when you want a defined industrial knowledge or workflow solution implemented for you.

Dust provides a workspace for companies to build internal AI agents, connect data sources and choose which models those agents use. It is a good fit for organisations that want a common agent platform and have a team available to configure and maintain it.

Opero takes responsibility for a narrower scope. We set up the technical document collection, parts data, service records and system connections needed for manufacturing use cases. Customers buy a working application and an implementation service rather than an internal agent-building environment.

## When Dust is the better choice

Choose Dust when your organisation wants to build a range of agents for several departments and retain hands-on control of their configuration. Choose Opero when the immediate requirement is technical knowledge or an industrial workflow and you want the implementation included.

## Related pages

- [Knowledge Agent](/product/knowledge) — how Opero handles manuals, citations and parts data.
- [AI Workflows](/product/workflows) — automation connected to the same knowledge base.
- [Trust](/trust) — hosting, data use and audit controls.

---

## Opero vs. Gemini (Google)

Source: https://www.opero.pro/compare/gemini

Choose Gemini for Google Workspace or a broad cloud AI platform. Choose Opero when you want a defined industrial knowledge or workflow solution delivered.

Gemini gives organisations a broad set of AI capabilities across Google Workspace and Google Cloud. Companies with an internal platform team can use those services to build agents, connect data and automate work throughout the business.

Opero is more specific. We configure the document processing, permissions and system connections needed for technical service and manufacturing work. This includes answers with source citations, access through chat or phone, and workflows that use the same knowledge.

## When Gemini is the better choice

Choose Gemini when your company works mainly in Google Workspace or wants a cloud platform on which an internal team can build many types of agents. Choose Opero when you want a technical knowledge or workflow application delivered and maintained for you.

## Related pages

- [Knowledge Agent](/product/knowledge) — cited answers from technical documents.
- [Trust](/trust) — Opero’s hosting and data-handling approach.
- [Why generic LLMs miss the mark](/resources/generic-vs-vertical) — more detail on the two approaches.

---

## Opero vs. Glean

Source: https://www.opero.pro/compare/glean

Choose Glean for company-wide search and agent building. Choose Opero for a delivered technical-service solution using industrial documents and processes.

Glean searches across a wide range of company systems and lets internal teams build agents on top of that search layer. It is designed to serve many departments and many kinds of company knowledge.

Opero covers a smaller domain in more depth. We organise OEM manuals, revisions, parts catalogues and service records, then configure the assistant and workflows used by technical teams. Voice access and manufacturing-specific processes are part of the implementation.

## When Glean is the better choice

Choose Glean when the main requirement is company-wide search across many departments and applications, especially if an internal team will build and maintain agents. Choose Opero for technical service and manufacturing use cases where the document structure, parts data and operational workflow matter.

## Related pages

- [Knowledge Agent](/product/knowledge) — how Opero works with technical documents and parts information.
- [AI Workflows](/product/workflows) — processes connected to ERP and service systems.
- [Trust](/trust) — hosting, permissions and audit controls.

---

## Opero vs. Guru

Source: https://www.opero.pro/compare/guru

Choose Guru for knowledge that owners write and verify as concise cards. Choose Opero for answers drawn from large technical document and service collections.

Guru gives companies a structured way to write, review and publish internal knowledge. Its card model is useful for policies, onboarding material, sales guidance and other information that an owner can keep up to date.

Opero is intended for technical collections that are too large or detailed to rewrite as knowledge cards. It indexes manuals, schematics, service bulletins and parts data, keeps track of document revisions, and cites the source used for each answer.

## When Guru is the better choice

Choose Guru when the knowledge is concise, changes at a manageable rate and should be reviewed by a named owner. Choose Opero when answers need to come directly from a large technical document collection. Some companies have both types of knowledge and use a separate tool for each.

## Related pages

- [Knowledge Agent](/product/knowledge) — cited technical answers and parts information.
- [Trust](/trust) — hosting, permissions and data handling.
- [Why generic LLMs miss the mark](/resources/generic-vs-vertical) — more detail on working with technical documents.

---

## Opero vs. Intercom (Fin)

Source: https://www.opero.pro/compare/intercom

Choose Fin for customer-support conversations at scale. Choose Opero when the answer depends on detailed technical documents, parts, or service systems.

Intercom Fin is built to answer customer-support questions and resolve conversations through support channels. It works with a company’s support content and is designed for teams managing a high volume of external enquiries.

Opero is used for technical questions and operational processes. A technician can ask about a machine, find the relevant part or start a workflow using information from manuals and service records. Customers can also expose selected technical knowledge through an Opero-branded support portal.

## When Fin is the better choice

Choose Fin when the main job is handling customer-support conversations at scale. Choose Opero when the answer depends on detailed technical documentation, parts data or an operational system. A manufacturer may use Fin at the support desk and Opero for the technical work behind it.

## Related pages

- [Customer Support Portal](/product/portal) — customer access to selected technical documentation.
- [Knowledge Agent](/product/knowledge) — cited technical answers and voice access.
- [Trust](/trust) — hosting, permissions and data handling.

---

## Opero vs. Perplexity

Source: https://www.opero.pro/compare/perplexity

Choose Perplexity for broad research across current sources. Choose Opero when answers must use approved company documents and continue into operational work.

Perplexity is designed for research. It searches across the web and connected sources, produces a concise answer and links to the material it found.

Opero works from a document collection prepared for a specific company. That matters when an answer must use the correct manual revision, follow existing permissions or refer to structured parts and service data. Opero can also use the answer in a phone conversation or an operational workflow.

## When Perplexity is the better choice

Choose Perplexity when someone needs to research a broad topic or find current information from many sources. Choose Opero when the source set must be controlled and the answer relates to the company’s own equipment, documents and systems.

## Related pages

- [Knowledge Agent](/product/knowledge) — how Opero organises and cites technical sources.
- [Trust](/trust) — access controls, hosting and data use.
- [Why generic LLMs miss the mark](/resources/generic-vs-vertical) — more detail on broad research and controlled company knowledge.

---

## Opero vs. ServiceNow

Source: https://www.opero.pro/compare/servicenow

Choose ServiceNow for a broad enterprise service platform. Choose Opero for a focused industrial process that works with technical documents and existing systems.

## Opero vs. ServiceNow

| Dimension | Opero (AI partner for industry and manufacturing) | ServiceNow (ITSM & Enterprise AI) |
| --- | --- | --- |
| **Best fit** | | |
| Target user | Service engineers, field teams and the customers they support | IT service desks & enterprise ops |
| Platform role | Technical knowledge and workflow automation, connected to your business systems (Customer-facing, Internal, Cross-system) | Enterprise service-management platform with workflows, knowledge, customer service, and AI capabilities (Enterprise platform, Service management) |
| **Technical knowledge** | | |
| Technical documentation *(unique)* | Configured for large collections of manuals, drawings, procedures, videos, and service records, with sources shown beside answers (Document scope, Source-cited) | Supports enterprise knowledge and conversational search, with its strongest fit in service-management processes (Service knowledge) |
| Technical doc specialization | Implementation includes organising OEM manuals, drawings, service procedures, revisions, and machine-specific information | Now Assist supports knowledge search and case work; industrial document structure depends on the implementation (Service-focused) |
| Knowledge capture & update | Interview staff, generate service reports, and keep documentation up to date with live conversations — via voice or chat (Voice capture, Chat capture, Auto-update KB) | Supports case summaries, resolution notes, and conversational knowledge search in service workflows |
| **Voice Interface** | | |
| AI voice agent | Dedicated phone number backed by an AI agent — technicians call to troubleshoot issues, file reports, and query the knowledge base hands-free (Phone number, Troubleshooting, Reporting, KB queries) | Customer-service voice features can assist live agents; the fit depends on the ServiceNow products and configuration used |
| **Workflow Integrations** | | |
| Connected workflows | Connects directly to Microsoft Dynamics, Zoho, e-conomic and more — auto-create invoices after a job, draft customer responses, trigger follow-up actions (Dynamics, Zoho, e-conomic, Auto-invoicing) | Mature service-management workflow platform with customer-service and field-service options (Service management, Product-dependent) |
| Named integrations | SAP · Microsoft 365 · Dynamics · Zoho · e-conomic · CMMS platforms · custom ERP/DMS — built to connect, not replace | IntegrationHub and product connectors support enterprise systems; availability and pricing depend on the ServiceNow package (Package-dependent) |
| RFP / RFQ automation *(unique)* | Reads RFP and RFQ documents, drafts sourced answers, and returns the response for bid-manager review (Doc-grounded, Auto-draft) | Can coordinate approvals and case work; a bid-response application would require configuration |
| **Customer-Facing Capabilities** | | |
| Customer portal *(unique)* | Deploy a branded customer-facing AI portal scoped to curated documents — customers get self-serve support without accessing internal knowledge (White-label, Scoped access, Branded subdomain) | Customer Service Management provides customer portals and knowledge features; technical product scope depends on configuration |
| Spare parts ordering *(unique)* | Customer describes a maintenance need → Opero surfaces the correct parts list → customer generates a formal order directly to the manufacturer | Can coordinate service and fulfilment workflows; parts catalogue and ordering depend on connected systems |
| **Implementation** | | |
| Time-to-first-value | A defined first process is typically prepared for production within six weeks, including guided setup and testing (< 6 weeks) | Timing depends on the existing ServiceNow environment, selected products, integrations, and rollout scope (Scope dependent) |
| **Business data controls** | | |
| Data used to train public models | Customer data is not used to train models; provider and processing terms are documented for the deployment | Enterprise policy — no training on customer data; Now Assist uses partner LLMs |
| Access and isolation approach | Separate customer environments, permission checks before model use, EU hosting by default, and private deployment options | Enterprise permissions, audit, and data-residency options depend on the ServiceNow products and region selected (Product-dependent) |
| Data residency & deployment | EU-hosted by default, with private and on-premises options available by agreement (EU, On-premise option, Contract-defined) | Regional hosting options depend on the ServiceNow product, customer location, and contract |

ServiceNow is an enterprise platform for IT service management, ticketing, change management and configuration data. Large IT organisations use it to standardise processes and manage services across the company.

Opero focuses on equipment and technical service. Its data includes manuals, schematics, parts, serialised machines and service history. The workflows may create a work order, prepare an invoice or help a technician diagnose a fault rather than manage an IT incident.

## When ServiceNow is the better choice

Choose ServiceNow for enterprise IT service management, a large internal help desk, ITIL processes or a CMDB. Opero does not replace those functions. Choose Opero for technical teams working with industrial equipment and the documents and systems around it.

## Related pages

- [Knowledge Agent](/product/knowledge) — cited technical answers, parts information and voice access.
- [Trust](/trust) — hosting, permissions and audit controls.
- [Why generic LLMs miss the mark](/resources/generic-vs-vertical) — more detail on software designed for a specific operational domain.

---

## Opero vs. Zapier

Source: https://www.opero.pro/compare/zapier

Choose Zapier for straightforward automation across many applications. Choose Opero when a process must first interpret technical knowledge or service data.

Zapier connects a large number of business applications and makes it straightforward to automate common tasks between them. It is particularly useful when a process has a clear trigger, a series of actions and standard data moving between SaaS products.

Opero adds a technical knowledge layer to the workflow. A process can begin with a fault report, a service manual or a parts question, use that information to make a decision, and then update the ERP or service system. Opero configures and maintains the document collection as well as the automation.

## When Zapier is the better choice

Choose Zapier for general application-to-application automation, especially when broad connector coverage and quick setup matter most. Choose Opero when the process depends on interpreting technical documents, parts data or service history before taking action.

## Related pages

- [AI Workflows](/product/workflows) — examples of automation based on technical knowledge.
- [Integrations](/product/integrations) — systems that Opero can read from and update.
- [Trust](/trust) — hosting, permissions and audit controls.