AI agents
Agents that read documents, query systems, draft work, and ask a person to confirm, placed inside your real workflows.
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.
How we roll it out
- Work and systems analysis. We map the workflow, the systems to connect, and where the data lives.
- Data work. We classify documents, make them searchable, and split access permissions.
- Agent design. We decide the tools and permissions the agent gets, and where a person approves.
- Isolated testing. We run it on real cases in an environment kept separate from operations.
- Rollout. We put it into the workflow, watch the results, and keep fixing.
Technology we work with
Commercial and open-weight models, self-hosted inference servers on vLLM (quantization, batching, multi-GPU), tool integration with Model Context Protocol (MCP) and LangGraph, vector search with pgvector and Qdrant plus rerankers, and the Temporal workflow engine. We build for replaceability so you are not locked into one model or vendor.