Shipping production AI in eight weeks, not eight months
A field guide to running tight AI sprints — from discovery to evals to a confident launch — without sacrificing reliability.
Most teams discover the hard truth about AI products only after they ship: the demo was the easy part. Real value lives in reliability, evaluation, and a feedback loop tight enough to catch drift before it becomes incident.
Over the past two years we have run more than forty engagements on a single rhythm — discover, prototype, evaluate, operate. The cadence is non-negotiable, but the artifacts inside each phase change to fit the problem.
Discover before you delight
We spend the first week mapping the actual workflow, not the wished-for one. Calls with three to five power users, a redacted sample of inputs, and a measurable definition of success — that is the entire artifact set.
Every engagement ends discovery with a one-page brief: the problem, the constraints, the success metric, and the smallest version of the product that will move it.
Prototype to retire risk, not to impress
Week two is a spike, not a demo. We pick the riskiest assumption — retrieval quality, latency, hallucination rate — and build the ugliest thing that can answer it.
If the spike fails, we change the plan. If it succeeds, we have earned the right to build for production.
Evaluate everything
By the time we are coding the surface, an eval harness already exists. Offline golden sets, online canaries, and a small panel of human raters keep us honest as the model and the prompts move.
Reliability is a product feature. We instrument it like one.