AI agents that do the work, not the demo
Most agent projects die between the prototype and production. We build the part that survives: tool contracts, retries, evaluation suites, cost ceilings, and a human in the loop exactly where it matters.
- 01A team spends hours a day on research, triage, or copy-paste between systems.
- 02Your prototype works in a notebook and falls apart on real inputs.
- 03You need an LLM feature that can be audited, not just demoed to investors.
Agent architecture
Planner/worker topology, explicit state machines, retries and hard stops. We pick the smallest design that solves your task and nothing more.
Tools & integrations
Typed function calls into your CRM, database, billing or internal APIs — with permissions, rate limits and an audit trail on every action.
RAG over your data
Chunking, embeddings, hybrid search and citation. Every answer points back at the source document, so a human can check it in seconds.
Evals & guardrails
Regression suites built from your real cases, cost and latency budgets per run, and alerts when output quality drifts.
- A running agent in your cloud or ours
- Eval suite + quality dashboard
- Measured cost per run, under an agreed ceiling
- Runbook, architecture notes and a handover session
Which models do you use?
Whatever fits the task and your compliance rules — Claude, GPT, or open-weight models on your own hardware. We keep the model layer swappable, so a price drop or a new release is a config change, not a rewrite.
How long until something runs in production?
Two to six weeks from kickoff to an agent working against production data, depending on how many systems it has to touch and how clean their APIs are.
Can it run without sending our data to a vendor?
Yes. We deploy open-weight models on your own GPUs or in your VPC — that's our self-hosted and private cloud service.