AI USAGE AUDITS FOR ENGINEERING TEAMS
Creating Order
from your team's AI usage.
We audit how your engineers actually use AI coding tools day to day through a scanner that runs on your own infrastructure. Your source code never leaves your premises: the scan itself makes zero network connections and you send us only a sanitised summary. We turn what we find into a series of detailed reports.
Real Data, Not Assumptions
We audit actual pull requests, codebases, and day-to-day AI usage across your team. Every finding links to a specific file and commit in your own codebase.
One Team-Wide Standard
Findings become a practical internal policy your whole team can follow - one shared standard for using Claude, Copilot, ChatGPT and the rest safely and consistently, instead of everyone improvising their own rules.
Less Waste, More Output
We flag the token spend and inefficient habits burning through your AI budget, and show your team how to get more out of subagents, skills, and context management.
How the Audit Process Works
On-Premises Scan
Your team runs a simple point-and-click app on your own infrastructure. It scans against 20 specific rule types across secrets, code quality, and AI-usage signals using only local Git commands. The scan itself makes zero network connections. Your source code never leaves your premises. You send us only one sanitised JSON file.
Findings Analysis
We map how your team actually uses AI day to day, tying every finding to a specific file and commit - never to a named individual. The analysis covers exposed secrets, hallucinated dependencies, co-authored AI trailers, untested code, and more.
Four Reports & Policy
You get four branded PDFs: a technical audit report, a management summary with urgency-ranked actions, a best practices blueprint for your leads, and a team-wide AI training guide. No developer is ever named in any of them. One practical policy your team adopts, for one flat fee.