An applied AI lab, not an agency.

Most AI systems work in a notebook and break under real traffic. Retrieval quality degrades as the corpus grows, evaluation is a spreadsheet of vibes, cost curves surprise finance in month three, and nobody can say whether last week's prompt change made the product better or worse. That gap — between a working demo and a system a business can depend on — is the whole of our work.

We have been shipping production LLM systems since GPT-3, before "AI engineer" was a job title, and have trained more than 300 engineers in the process. Engagements fall into three shapes: production architecture for RAG, agents, and eval loops that hold under load; technical due diligence for funds and acquirers who need to know what is actually under the hood before they wire money; and fractional AI leadership for teams that need senior technical direction without a $500K hire.

We work engineer-to-engineer. No decks that outlive the code, no discovery theatre, no framework of the month. You get architecture decisions with the tradeoffs written down, evaluation harnesses your team can run without us, and the capability to keep going after the engagement ends. If a project is not a fit, we say so on the first call rather than sell into it.