What a Useful AI Evaluation Looks Like for a Small Business
Small businesses don't need leaderboard-style evaluation โ they need a lightweight, repeatable scorecard that measures what their actual workflow must not get wrong.
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Small businesses don't need leaderboard-style evaluation โ they need a lightweight, repeatable scorecard that measures what their actual workflow must not get wrong.
The more useful question than "which model is best?" is which combination of model, evaluation, workflow design, and cost controls produces a dependable result for this job.
Standardizing on one "best" model is becoming one of the most expensive decisions an AI team makes. A routing layer sends each task to the cheapest model that clears the quality bar.
The gap between a promising AI demo and a dependable production workflow is infrastructure โ retries, fallbacks, idempotency, checkpoints, and human review. That is where ROI is made or lost.
The U.S. framework for frontier models is voluntary and unpublished โ which makes it a useful case study in proportionate governance, not a reason for theatre.
The gap between cached and uncached input pricing is the largest pricing lever most teams never touch. Designing for cache hits is architecture, not a billing footnote.