AI Agents
AI teammates that work inside the systems your business already runs on. Trained on your data, observed with audit trails an engineer can read, handed over with the keys.
AI teammates that work in your stack, not next to it.
What we ship
- 01
One agent, one job — customer triage, intake, scheduling, ops
- 02
Trained on your data, deployed in your cloud
- 03
Human-in-the-loop fallback for every uncertain step
- 04
Audit trails an engineer can read, long after handover
The queue is pinned. The hiring req has been open six months. Every week the team triages the same intake forms, schedules the same calls, answers the same question about an order that shipped Tuesday.
Stop hiring for the broken thing. Not a general-purpose chatbot — the specific worker the team has been waiting for, calibrated against real cases, deployed where the work happens, observed long after handover.
Six weeks from the queue to a worker who knows the job.
- 01 SAMPLE
Pull real cases
A hundred to five hundred actual transcripts, tickets, intake forms. Not synthetic data, not the demo set.
Week 1 - 02 CALIBRATE
Build against the sample
First working agent inside two weeks. Evals on real cases, not toy benchmarks.
Week 2–3 - 03 PILOT
Run alongside the team
Shadowed against real cases — read-only until the eval scores hold. Confidence threshold tunable; audit trail on every action; the human takes the gray-zone tickets.
Week 3–4 - 04 SHIP
Deploy in your cloud
Observability, redaction, runbooks. Handover with the keys, not the password.
Week 5–6
What we measure
- Resolution rate
- P95 latency
- Cost per run
Adjacent practices
Bring us the problem.
30-minute call. One-page proposal by Friday. We respond within one business day — every time. If we're not the right fit, we'll tell you who is.