A clear-eyed audit of
how your team actually uses AI
We review real pull requests, real codebases, and real day-to-day usage of tools like Claude, Copilot, and ChatGPT - then turn what we find into a code of conduct your whole team can actually follow.
Core Competencies
The four pillars of architectural trust. We apply engineering rigor to the volatile nature of neural networks.
PR & Codebase AI-Usage Scan
We go through recent pull requests and codebase history to see what's actually being pasted into AI tools - flagging credential and secret exposure risk, and proprietary code leaking out into third-party models.
- warningChecks for exposed secrets
- lock_openChecks for proprietary code leaks
- commitReviews pull request history
Team Usage-Pattern Audit
We map real behavior across the team - who's careful, who's throwing everything at AI without judgment, who's avoiding it out of fear, and who's using it responsibly but inefficiently.
- local_fire_departmentFlags AI overuse
- blockFlags AI avoidance
- hourglass_bottomFlags inefficient AI usage
AI Code of Conduct Design
Findings get turned into one practical internal policy - a shared standard for how the team uses AI tools safely and efficiently, written to actually get adopted, not filed away.
- descriptionDrafts a practical policy
- groupsPlans for team adoption
- summarizeDelivers a final audit report
Ongoing Compliance Retainer
Once the policy is in place, a monthly retainer keeps checking that it's holding - catching new leakage, new overuse, or new waste as your team and its AI usage grow.
- calendar_monthMonthly policy check-ins
- radarDetects new leakage
- savingsTracks AI spend and waste
Ready to audit your infrastructure?
Our initial consultation includes a structural risk assessment and a preliminary mapping of your model's legal footprint.