Happy to answer any questions about the approach. The key insight is that conformal prediction gives you calibrated confidence intervals — if we target 80% coverage, we actually get 81% of actual costs landing within the predicted range. No ML runtime needed, just feature extraction + dot product. Runs in ~5ms per estimate.

Biggest limitation right now: it only sees the prompt text, not your codebase or conversation history. Short/vague prompts get wide ranges.