From idea to production
AI Product Build
Our end to end process for shipping an AI product or feature: scope the risky part first, build on proven rails, launch with evals and guardrails in place.
The sequence
Why this workflow exists
AI features fail differently from normal features. They fail probabilistically, which means the usual test suite passes while real users get bad answers. The process has to account for that from the first week rather than the last.
The steps in detail
Isolate the risky judgement
Every AI product has one hard call at its center. We prototype that alone, against real data, before committing to anything around it.
Build a labelled eval set
Fifty to two hundred real cases with known good outputs. This becomes the definition of done and the regression gate.
Stand up the boring rails
Auth, storage, billing, queues, and deployment on frameworks we have shipped before, so the interesting problem gets the interesting time.
Integrate with guardrails
Confidence thresholds, refusal behavior, human escalation, cost ceilings, and structured logging on every inference.
Launch narrow, widen on evidence
First release covers the case types the eval data proves out. Coverage expands weekly based on production traces.
When this is the wrong choice
Every workflow has conditions where running it makes things worse. These are ours.
- There is no labelled data and no willingness to create any, since evals are non negotiable
- The use case needs deterministic guarantees that a language model cannot give
- The real problem is a data quality problem wearing an AI costume
Outcome. A launched feature with measured quality, bounded cost per run, and a regression gate that catches degradation before customers do.
- CategoryBuild
- Scope5 steps, 3 to 8 weeks
- Human in the loopYes, at every gate that matters
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The quote tells you whether this workflow fits, what it costs, and where it would break in your specific environment.