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AI implementation

Turn a promising AI idea into a reliable part of daily work, with clear quality checks and human oversight.

Discuss this service
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Rūdolfs Klintsons / Pexels

When this fits

  • Finding answers from your organisation's own information
  • Handling documents or messages
  • AI-assisted product features
  • A pilot that needs to become a dependable service

What we can help with

Choose where AI can help

  • Reviewing the current work and available information
  • Choosing suitable AI tools
  • Defining the use case and success measures

Build the solution

  • AI features in digital services
  • AI assistants that answer from your own information
  • Automating documents and multi-step tasks

Make it dependable

  • Testing answer quality and human review
  • Clear limits and fallback practices
  • Monitoring quality, costs and response times

What’s included

  • A clearly defined task and measures of success based on daily work
  • Realistic test examples and an agreed way to assess results
  • A working solution with clear backup and review steps
  • Guidance for monitoring quality, costs and problems

How we work

  1. Frame and evaluate

    Choose a useful task, create test examples and agree what good enough means.

  2. Put it into daily work

    Connect the solution to the real task and add the checks it needs.

  3. Observe and improve

    Track quality and failures so the system can be improved responsibly.

Frequently asked

Do we need to know which AI tool to use?

No. We choose the tools based on the task, quality needs, what information can be used and where the solution will run.

How do you assess quality?

We use representative examples and explicit criteria, then review both average performance and important failure cases.

Can a person remain in control?

Yes. Review and approval steps are designed around the impact of a wrong answer or action.

Bring the situation. We’ll frame the next step.

We can define the sensible next step together.

Tell us what you need