Evals are the moat for AI guidance products

Every team building an AI guidance product has access to the same frontier models. The weights are not your advantage. What is hard to copy is knowing, precisely, what “good” means for your users, and being able to prove you hit it release after release.

An eval is a contract, not a vibe

An eval pairs a real scenario with the behavior you expect and a way to grade whether you got it. On its own, one eval is a unit test for a moment that matters. A set of them is a contract for how your product behaves when it counts.

The work is turning your method into that contract: the archetypes that recur, the moves that are right for each, and the failures you can’t tolerate. Done well, “did that feel better?” becomes “did that meet the bar for the user we meant to help?”

Why it compounds

The first eval is just a test. The hundredth is a moat. Every failure you catch in production becomes a permanent check that the next change has to pass, so the same mistake can never ship twice.

A competitor starting today has the same model you do, and none of those lessons. Your eval suite is the asset that actually compounds: a record of everything you’ve learned about your users, enforced automatically.