AI agents

Agents that attach to work, not demos

A conversational demo takes a day. The hard part is making the agent pull accurate data from internal systems, setting boundaries so it does not produce wrong answers, and building the flow where a person reviews and approves the result. We consider that last step part of the deliverable.

How we build

• Narrow scope. One task, one team. Expand once results are visible.
• Tools first. Define the query, draft, and send tools the agent may call, with the minimum permissions.
• Evaluation as code. Build an evaluation set from real cases and verify automatically whenever the model or prompt changes.
• A seat for people. Decisions that need confirmation always route to a person.

Technology we work with

Claude and GPT model families as well as open-weight models, Model Context Protocol (MCP) tool integration, vector and hybrid search, workflow engines, and observability tooling. We build for replaceability so you are not locked into one model or vendor.