You have a product. You want AI to do something specific inside it. We help you figure out what that looks like, build it, and make sure users can actually work with the output. This is hands-on work, not a deck.
For teams adding AI to an existing product or workflow
End-to-end design and development work for teams adding AI capability to an existing product. We start by scoping what AI should actually do — what the user needs, what the model can reliably deliver, and where those two things meet. Then we design and build the interface and integration layer that connects them.
This is not a strategy document or a proof-of-concept demo. It is the actual work of making AI functional inside your product in a way that holds up under real use. That means thinking carefully about the user-facing layer, the failure states, and the feedback loops that let users know when to trust the output and when to verify it.
A healthcare startup has a working NLP model that extracts structured data from unstructured clinical notes. The model is solid. There is no interface for the review step. Clinicians need to verify, correct, and approve extracted data before it flows into downstream systems, and right now that happens in a spreadsheet.
We would design and build the review interface. The core problem in a setup like this is correction confidence. Clinicians need to trust that what they approve is accurate and that corrections will propagate correctly. That means a side-by-side view of the original note and the extracted fields, inline editing, field-level confidence indicators, and a clear approval state. Nothing that requires a training session to understand.
This is hands-on work. We scope it, design it, build it, and iterate until it holds up in a real clinical workflow.
Scope defined before work begins. No open-ended billing.