The ops layer under any AI system you deploy: eval pipelines, observability, guardrails, and red-team surfaces. If you're running AI in production, this is what you don't want to build yourself.
Prompt versioning. Eval harnesses. Observability. Guardrails. Rollback. Cost tracking. Each team writes their own, because the first AI feature shipped before anyone asked "who owns the platform?" By the third feature, the team is firefighting. AI platform engineering is the ops layer that shouldn't be left to the product team. It's what separates "we have an AI prototype" from "we operate an AI service in production".
Not every team needs all six on day one. But the team that has them by AI feature number three ships faster than the team still rebuilding them on feature number twelve.
// platform coverage — prioritized per engagement
// prioritize by your actual risk profile. we help pick.
A platform nobody uses is worse than no platform. We build what's missing, using what you have, with adoption paths that don't require the whole team to re-learn their workflow.
We look at what you have — which teams, which tools, which pain points — and identify where the platform earns ROI fastest. Usually evals and observability. Output is a phased plan, not a big-bang platform vision, because big-bang platforms don't get adopted.
We use what exists where it fits (LangSmith, Langfuse, W&B, your current CI) and build custom where your integration is unusual. Each piece ships usable on its own. First working eval harness in 2-3 weeks; full platform in 2-3 months; adoption paths documented at every step.
We can run the platform for you, or hand it to your team with runbooks and office hours for the first 90 days. No proprietary lock-in — the platform runs on your infra and uses open tools where possible.
Free initial scoping — 30 minutes to tell you which layer is most load-bearing right now and what a phased build plan looks like.