The problems we solve, in practice.

The challenge. A founding team had a compelling AI prototype built in a no-code AI tool. It won meetings and fell apart the moment real users and real data arrived: no evals, secrets in the client, no way to change a prompt without breaking three others.

What we did. We rebuilt it as a production system — proper architecture, an evaluation harness around the core AI behavior, guardrails and auth, and observability into every model call — without losing the momentum the prototype had created.

The challenge. An existing product team knew AI mattered but was stuck debating models and features with no shared thesis. Every idea was a feature; none was a strategy.

What we did. We ran a focused framing engagement: identified where intelligence created real leverage in the workflow, defined the AI surface and the data and feedback loops it needed, and produced a costed, sequenced roadmap from prototype to production.

The challenge. A retrieval assistant was live but wrong often enough that users stopped relying on it. There was no way to measure quality, so every fix was a guess.

What we did. We built an evaluation dataset and scoring for the assistant, re-architected retrieval and prompting against those evals, and added guardrails and citations so answers were verifiable — turning "hope it works" into a number we could move.