Inference-cost audit

Most production LLM calls return a label, not prose — a route, a score, a yes/no, a category. Those calls pay for autoregressive generation they do not use. This audit finds them and prices the swap.

Deliverable: a ranked table of candidates with measured deltas. Sellable as a OneWave engagement; same shape as marketing-brain — a repeatable audit run per client.

1. Find the candidates

Search for LLM calls, then filter to the ones whose output is a label:

grep -rnE "messages\.create|chat\.completions|generateText|\.invoke\(|anthropic\.|openai\." \
  --include=*.{ts,tsx,js,py} . | grep -v node_modules

A call is a candidate when all of these hold: