Assistants, agents and retrieval systems built into products people already use, with the evaluation and guardrails that keep them trustworthy after launch.
The AI layer
Intelligence inside the product your users already open: drafting, summarising, searching and answering in the flow of the work.
Multi-step agents wired to your tools and APIs, with permissions, human review gates and a record of every action taken.
Your documents, tickets and databases made answerable, with citations back to source so people can verify what they are told.
The unglamorous half. Test sets, accuracy scoring, red-teaming, cost per call and drift alerts running continuously.
We tell you when AI is the wrong tool
Most AI projects fail on data, not on models. Every engagement opens with a four-part readiness score, and a low score means we recommend plain software instead.
Stack
Common questions
Often it is not, and we will say so. Discovery scores your use case on data readiness, tolerance for error and cost per call before anyone writes code.
It stays yours. We deploy inside your cloud account or use zero-retention endpoints, and we never train shared models on client data.
Retrieval with citations, output validation, and an evaluation suite that runs on every change. Where accuracy matters most, a human approves before the action commits.