AI in product delivery

A convincing prototype is not a product. We anchor work in measurable outcomes — latency budgets, support deflection, or conversion — and design the smallest slice of automation that can move those numbers. Everything else waits.

• Define the metric before you choose a model or vendor.
• Time-box discovery so “research” does not swallow the roadmap.
• Prefer a boring baseline you can ship this month over a perfect future state.

If it is not tied to a product outcome, it is a side project — even when the demo is dazzling.

If you cannot tell whether outputs are improving week over week, you are flying blind. We treat evaluation datasets, human review loops, and regression checks as first-class deliverables, shipped alongside the UI.

• Golden sets for the flows that matter most (not the whole universe).
• Human labels on a steady cadence, not one-off audits.
• CI checks that fail when quality drifts after a dependency or prompt change.

Shipping without evaluation is shipping hope — and hope is not a release strategy.

High-stakes flows need clear escalation paths. We design UX and APIs so operators can override, annotate, and feed corrections back into the system without filing a ticket to engineering.

• Explicit “not confident” states instead of silent wrong answers.
• Audit trails that product and compliance can actually read.
• Feedback channels that close the loop into training or prompts.