Evaluation, not modelling, is the hard part
The industry has converged on a quiet consensus: the difficulty in shipping AI systems is knowing whether they are getting better, not making them better.
Public benchmarks saturate quickly and correlate weakly with production performance. Teams that rely on them ship regressions confidently.
The alternative is unglamorous: domain-specific evaluation sets built from real traffic, graded by people who understand the domain, refreshed as the distribution drifts, and run on every change.
We treat evaluation as a first-class deliverable in each module's scope rather than a testing afterthought. Each module ships with its own graded set and a regression gate that blocks promotion.
This is a meaningful share of engineering effort. It is also the only reliable defence against the failure mode where a system quietly degrades while every dashboard stays green.
Investor takeaway
Domain evaluation sets are the only trustworthy signal that an AI system is improving.