Loading page
Agents that hold context across long-running tasks, call your internal APIs, and know when to escalate to a human. Not a chatbot with a system prompt — a supervised worker with tools, memory and an audit trail.
Scoped to reach production, with the evaluation criteria agreed before a line of code is written.
Autonomous agents that plan, use tools and finish the work.
Agents that hold context across long-running tasks, call your internal APIs, and know when to escalate to a human. Not a chatbot with a system prompt — a supervised worker with tools, memory and an audit trail.
Tool-calling & function execution
Multi-agent orchestration
Human-in-the-loop checkpoints
The same sequence on every engagement, whether it runs nine weeks or nine months. Nothing carries over on trust alone.
01Discover
A paid two-week audit of your processes, data and systems. We come back with a ranked list of what AI should touch — and what it should not.
Audit report and ranked backlog2 weeks
02Architect
Model selection, retrieval design, guardrails and the integration surface. You get a written architecture with a cost model attached to it.
Architecture doc with cost model1–2 weeks
03Build
Two-week sprints with a working agent at the end of each one. Your team reviews real output, not slideware, from the third week onward.
A working agent every sprint4–8 weeks
04Evaluate
A task-specific eval suite gates every release. Nothing reaches production until it beats the baseline on your data, not a public benchmark.
Eval suite and release gateContinuous
05Operate
Tracing, drift alerts, cost dashboards and an on-call team who already know your stack. We stay after go-live — that is the whole point.
Live dashboards and on-call rotaOngoing
Bring one process you think an agent could run. We'll tell you straight whether it's worth building — and what it would cost if it is.