AI integration

Another interface is not always the answer. Integration brings suitable AI capabilities into existing workflows, using your data sources, permissions and approval steps.

Where it helps

  • Add knowledge search to existing applications
  • Classify documents and extract information
  • Prepare text and summaries within the workflow

What the project includes

  • Review of available APIs and permissions
  • Model evaluation against your test cases
  • Integration with error handling and cost controls
  • Documentation of data flows and dependencies

Questions to prepare

  • Which interfaces are actually available?
  • Which data may be sent to a model?
  • How should the workflow behave if the provider is unavailable?

Test before daily work depends on it

Acceptance uses agreed examples from your workflow, including incomplete inputs and common exceptions. We check the intended result as well as whether failures remain visible and the process hands control to a person when needed. Owners receive a documented basis for deciding whether to put the solution into use.

Deliberate boundaries

Scope and availability depend on your systems’ interfaces. Providers, data locations and model terms are agreed before use.

Scope, price and timing

After an initial conversation, we clarify prerequisites and prepare an individual proposal. Pilot scope, acceptance criteria and timing are agreed before starting.

How we approach it


01

Meet

We discuss your process, the people involved and the intended outcome.

02

Understand & define

We review data and interfaces, then agree on success criteria.

03

Pilot & decide

A bounded pilot shows what works and what needs to change.

04

Introduce & support

We integrate the solution, document it and agree how it will operate.

Which process takes too much of your team’s time?

Tell us about it. Together, we can find a concrete starting point.

Discuss a project