My CTO dropped a rule I hadn't heard before: "Never build a solution that AI companies will ship themselves in the next six months."

We were talking about traceability. Every team building with Claude or GPT hits this wall: something goes wrong in production, and you can't trace why.

So instead of building immediately, we researched. Mapped 20+ tools, $600M+ in funding, two regulatory frameworks. Applied the 6-month rule to every potential feature.

What we found: the gap between "we have traces" and "we can satisfy a regulator" is filled by consultants and spreadsheets. No tool translates LLM traces into compliance evidence. Providers won't build it (liability). Observability tools won't build it (wrong buyer). GRC platforms start from policy, not data.

We built a CLI that fills it:

aitrace audit traces.json -r "EU AI Act" -o report.md

Then we pointed it at ourselves. 590MB of Claude Code trace data sitting in ~/.claude/ that we'd never examined. 16,413 AI calls. $3,892 estimated cost. What we learned:

- 96.4% of input tokens are cache reads. Caching saved $11,562.
- Sessions under 30 minutes are 2x more token-efficient than 8-hour marathons.
- CLAUDE.md reduces edits per file by 24%.
- One file was edited 87 times across 3 sessions. It needs to be broken up.
- Usage is trending up 94%. Forecast: $6K/month at current pace.

7 commands total: compliance auditing, usage insights, session health scoring, workflow optimization, agent delegation analysis, and cost forecasting. All runs locally. Zero cloud. The data never leaves your machine.

That last part is the moat. Every competitor who tries this needs to collect your data. We analyze it where it lives.

pip install ai-trace-auditor

Full landscape analysis + build story: https://bipinrimal.com.np/blog/020-the-6-month-rule
GitHub: https://github.com/BipinRimal314/ai-trace-auditor

#AICompliance #EUAIAct #LLMObservability #OpenSource #ClaudeCode #AIGovernance
