03 — AI WORKFLOWS
AI workflows that survive contact with production
Anyone can make a model demo well. Making it behave the same way on the ten-thousandth run — with your data, your edge cases and your compliance rules — is engineering. We build LLM pipelines the way infrastructure should be built: evaluated, guarded, observable, and boring in the best possible way.
What you get
- RAG systems grounded in your own documents and data, with citations you can audit
- Eval suites that measure quality before your customers do — regression-tested like real software
- Guardrails for tone, safety, cost and hallucination, tuned to your risk tolerance
- Observability dashboards: what the model did, why, at what cost, and where it is drifting
How it works
- Map the genius. We audit how your team actually works — and find where intelligence compounds fastest.
- Build the agents. Design, automation and agents ship in weekly increments. First agent live in 14 days.
- Multiply. Your team reviews, agents execute, output compounds. We stay until it runs itself.
See the full process on the homepage, or how it played out for a real client in the Puffsy case study.
Frequently asked questions
Which models do you work with?
We are model-agnostic: Claude, GPT, Gemini and open-weight models — often mixed, routing each task to the cheapest model that clears the quality bar your evals define.
How do you stop the model from making things up?
Grounding (RAG with citations), guardrails that reject unsupported claims, and evals that measure hallucination rates over time. No system is perfect; ours tell you when and where they fail.
Can you fix an AI feature we already shipped?
Yes — rescue work is common. We usually start by adding evals to quantify the problem, then iterate on retrieval, prompts and guardrails until the numbers hold.