Retrieval, agents, model integration, and the plumbing around them. We build AI features the team is willing to keep on in production.
We start by writing down the workflow the AI is meant to compress. If the workflow is not legible on paper, no model will save it.
Every shipped AI feature needs a way to know whether it is getting better or worse. We build the harness alongside the feature.
Retries, caching, rate limits, deterministic fallbacks. The model is the smart layer. Everything around it is the engineering.
A team wants to ship one well-chosen AI surface without disrupting the roadmap. We pick the leverage point and build it.
Internal documents, support tickets, code. We index, evaluate, and wire the surface into the existing product.
A multi-step internal process that could be partially automated. We build a careful, observable pipeline rather than a flashy demo.