What we solve
An AI experiment has shown value, but staff still copy data by hand and switch between interfaces. We place the feature where work happens while respecting the system’s architecture and constraints.
What you get
One or more AI features become part of an existing process: they receive permitted context, return results to the right place, and leave an auditable record of actions.
What is included
- Review of the process and technical environment
- Model selection and boundaries for the AI feature
- An integration layer and error handling
- An interface for human confirmation
- Testing, monitoring, and documentation
How we work
- Check the existing system’s API, data, permissions, and constraints.
- Isolate one priority use case.
- Build the integration in a test environment.
- Roll out in stages and compare results with the original process.
Optional additions
- A fallback model or manual route
- Request limits and cost controls
- A quality dashboard
- User training
Important considerations
Timeline and scope depend on the documentation, API, and access rules of the existing system. If integration requires its developer’s involvement, we make that boundary clear before work begins.