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02 / AI integration for business

AI integration. With a clear purpose.

Assistants that find knowledge, process documents and draft replies. Start with a specific task and test quality on your examples.

  • AI assistants
  • Knowledge search
  • Document processing
Discuss my AI taskExplore scenarios
The project journeyClick or turn the cube
PROJECT STAGES01 / 04
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01 / Discover

Choose a task and define a useful outcome.

  • Pilot before rollout
  • Test on your examples
  • Human review where it matters

01 / Scenarios

Where could AI lighten the load?

Choose a workflow. Each one defines what the model does, which sources it uses and when a person takes over.

Company knowledgeDocumentsCustomer enquiries

Illustrative workflow / 01

The answer exists. Finding it is hard.

Procedures live in folders and instructions get buried in chat. Colleagues keep answering the same questions.

  1. Connect

    Index agreed documents while respecting access permissions.

  2. Find

    Retrieve relevant passages and draft an answer with source references.

  3. Verify

    Say when information is missing and point the user to a specialist.

What changes

People get a starting point and can check the original source.

How to assess value

Answer accuracy · reference quality · search time

Illustrative workflow / 02

Read. Extract. Enter. Repeat.

People manually extract names, amounts and terms from documents. The backlog grows faster than the time available to review it.

  1. Extract

    Read documents and extract required fields into an agreed format.

  2. Check

    Validate required values and send uncertain results for human review.

  3. Transfer

    Move approved data into the accounting system or the next workflow.

What changes

People review prepared data instead of entering everything from scratch.

How to assess value

Field accuracy · manual corrections · handling time

Illustrative workflow / 03

Questions repeat. People get tired.

Support teams spend time finding instructions, classifying tickets and writing similar first replies.

  1. Understand

    Identify the topic and retrieve relevant material.

  2. Draft

    Prepare a reply using the context and company guidelines.

  3. Approve

    A person reviews the draft. Complex cases go to the right specialist.

What changes

Support gets a useful first draft while the team stays in control.

How to assess value

Drafting time · corrections · routing accuracy

02 / The deliverables

A useful assistant. Quality you can check.

Bring AI into everyday tools with clear boundaries around what it can do.

  1. A focused pilot

    One workflow, representative examples and a way to compare it with the current process.

  2. Quality evaluation

    Typical and difficult examples, acceptance criteria and an error review before rollout.

  3. Data and permissions

    Agreed sources, update rules and limits on information access.

  4. Operational visibility

    Model costs, error logs, feedback and a process for updating the solution.

03 / An easier place to start

Prove the value of one task first.

Choose a task worth speeding up. Prepare examples and define what makes a good answer, what is unacceptable and where a person is needed.

Discuss my AI task

For our first conversation

  • Who will use the assistant
  • Sample questions or documents
  • Your definition of a useful result

What happens next

A pilot plan, quality criteria and a build estimate. Model and infrastructure costs are estimated separately.

Scope depends on data quality, integrations, access rules and validation effort.

04 / Questions

Before we begin

Budget, data and the details worth discussing early.

Do we need to train our own model?

Not always. An existing model, retrieval over your documents and careful evaluation may be enough. Model selection or fine-tuning depends on your task, data and constraints.

Where will our documents go?

We agree on this before connecting data: providers, storage locations, retention and access rules. Where needed, we assess deployment within your infrastructure and the resources it requires.

Can AI be added to an existing product?

Yes, for example search, suggestions or draft responses. Start with one workflow, agree on the integration and test its impact on speed, cost and the user experience.

How do we test an AI assistant before rollout?

Collect typical and difficult questions, expected answers and examples where the assistant should say that evidence is missing. Check accuracy, citations and how many answers require human correction during the pilot.

What affects the cost of AI integration?

Data sources and quality, access rules, integration work and validation requirements determine build scope. Estimate model and infrastructure costs separately at the expected usage level.

What data do we need for an initial discussion?

Describe the users and the task the tool should speed up. Anonymised samples of questions, documents or enquiries, plus criteria for a useful answer, are enough to start.

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