Production the Claude API, shipped in 14 days.

A model call is the last resort, not the first

The most expensive mistake in an AI feature is asking the model to do work that a database query, a deterministic function, or a simple rule should do. We design the system so the model does only the part it is genuinely good at: reading context and making a bounded judgment.

In our ApplyAI sprint, retrieval was a Postgres query and the Claude call only ranked the shortlist. That split is what kept the feature cheap, fast, and honest.

Features that know when to refuse

A good AI feature has a confidence gate. When the model is not sure, it should say so and hand off to a human or a fallback flow. We build that gate first and test it with an adversarial eval set, half of whose questions have no good answer. The feature has to refuse all of them.

What a Claude API sprint typically covers

• A retrieval (RAG) layer grounded in your own documents, with citations.
• A bounded agent that ranks, classifies, or drafts, with a confidence gate.
• An eval harness with graded examples your team can extend.
• A cost and latency pass to bring an existing AI feature into budget.