AI doesn't fail catastrophically — it drifts. Subtle errors compound silently across your codebase, your content, your operations. Most organizations can't quantify the cost. We can. We built the measurement framework across 596,903 lines of production code.
A correct implementation and an incorrect one arrive with identical formatting, identical tone, identical certainty. The AI doesn't flag its own uncertainty reliably. Roughly 12–15% of what AI reports as complete requires correction.
This isn't a deficiency to eliminate — it's a structural property to manage. The organization that expects 100% accuracy is perpetually surprised. The organization that budgets for the Drift Tax absorbs corrections as routine.
The first step is measurement. You can't manage what you haven't quantified.
Every audit delivers quantified findings across these areas — not opinions, not frameworks, not best practices. Numbers.
We classify your AI-assisted outputs — code commits, content, operational decisions — into clean vs. drift categories. You get your actual drift rate: the percentage of AI output that required correction, rework, or reversal.
Drift isn't free. Every correction costs engineering time, review cycles, deployment friction, and opportunity cost. We translate your drift rate into dollars — what you're actually spending on AI-generated rework per sprint, per quarter, per year.
When drift happens — and it will — what catches it? We assess your current detection and recovery mechanisms: code review, testing, QA gates, monitoring. Most organizations have no structured recovery architecture for AI-specific drift patterns.
Your numbers against ours. We've documented drift rates, rework trajectories, and recovery patterns across 10 production systems, 2,561 commits, and 596,903 lines of code. You see exactly where you stand relative to a measured, production-validated benchmark.
We define the audit perimeter — which teams, which workflows, which AI tools. You provide access to commit history, review data, and output samples. Typical setup: 2–3 days.
We analyze outputs using the same commit-classification framework validated across the CEM portfolio. Every output is categorized: clean execution, drift requiring correction, or rework. No subjective scoring — binary classification with traceable methodology.
You receive a comprehensive report: measured drift rate, dollar cost of rework, recovery gap map, and CEM benchmark comparison. Plus actionable recommendations ranked by impact and implementation cost.
This isn't a consulting framework built from theory. The Drift Tax measurement methodology was developed across 10 production systems, 2,561 classified commits, and 596,903 lines of code — all documented, all git-verified, all peer-reviewable.
The white paper — The Drift Tax: Structural Error Rates in AI-Native Execution — is the first empirically documented measurement of AI drift cost from production-scale execution. It's the foundation of every audit we deliver.
Read the white paper (TP-06)Scope depends on team size, number of AI-assisted workflows, and depth of analysis. Every engagement includes all four deliverables — drift rate, rework cost, recovery gaps, and CEM benchmark comparison.
Typical audits for teams of 5–20 engineers range from $10K–$25K. Enterprise assessments with multiple teams and ongoing monitoring are scoped individually.
Tell us about your team and we'll scope the engagement. Response within 24 hours.
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Expect a scoping response within 24 hours.