AI software that reads, decides
and acts on your data

We work with leading models such as OpenAI’s GPT and Anthropic’s Claude, choosing per task — typically a fast, low-cost model for sorting and extraction and a stronger one for analysis. Where it makes sense, we keep the model layer separate so switching providers later doesn’t mean a rebuild.

Yes. We connect it to your documents, databases, inboxes and APIs, and it retrieves only what each task needs. Answers can point back to the record they came from, so your team can check them instead of trusting them blindly.

No model is right every time, so we design for that: test sets built from your real cases, rule checks on every output, confidence thresholds, and human approval on anything high-stakes. Sentinel’s autopilot, for example, has to prove itself on paper before it is trusted with a live account.

Only where you decide it should. Most projects start with the AI preparing a decision and a person approving it. As it proves itself on your data, you can let it handle more automatically — always inside limits you set.

API keys stay on the server, access is role-based and important actions are logged. Where your data is hosted and which model providers process it is agreed up front in your proposal, so there are no surprises.

It depends on scope. We typically start with a focused prototype on your real data so you see results early, then build the full product around it. The timeline for each phase is agreed in your proposal.

Real usage always teaches something. We watch quality and running costs, tune prompts and rules as new cases come in, and extend the system when you are ready. The support arrangement is agreed in your proposal.