Tool
AI feature launch checklist →
A scored, adaptive checklist for shipping an AI feature without breaking trust.
06 · Pillar
Shipping AI Safely
For the people deciding whether — and how — to add AI. The product- and leadership-level view: launch checklists, "passing tests isn't proof," build-vs-buy, cost, rollback plans, and hiring for AI quality.
Case study
Case studies →
Real before/after outcomes, each linked to its verifiable receipt.
Tool
Cost-of-bad-AI ROI calculator →
Model the annual exposure of an unproven AI feature — and what a gate recovers.
Reference
Hiring an AI QA engineer →
What to look for when hiring for AI quality.
Reference
What an LLM evaluation consultant does →
The role, the scope, and when you actually need one.
Service
AI product build (service) →
From idea to a shipped, proven AI feature.
Guide
How to Ship an AI Feature Without Breaking User Trust →
Guardrails, disclosures, and fallbacks that protect user trust in AI features.
Guide
Passing Tests Is Not Proof: What Done Means for an AI Feature →
Why green tests are not proof for AI, and a real definition of done.
Guide
When Your AI Works in Demo but Fails in Production →
Why AI features pass the demo but fail in production, and how to close it.
Guide
Build vs Buy: LLM API vs Fine-Tuning Your Own →
A framework for choosing between an LLM API and your own model.
Continue across the library
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