Diagnose Structural Failure in AI-Generated Codebases
Forensic diagnostic for applications built with Cursor, Lovable, Bolt.new, v0, and similar tools. Identify architecture drift, regression risk, and structural instability — before production failure.
Used to diagnose AI-generated codebases across SaaS, internal tools, and production applications.
Architecture patterns documented in the ASA Standard.
Quick Scan — Structural diagnostic of your repository
Delivered in 24 hours. Fixed price. No commitment required.
AI Chaos — the structural cost of prompt-driven development
AI-generated codebases often function correctly in early stages.
Structural instability emerges later, when prompt-driven changes accumulate faster than architectural boundaries can contain them. Each session optimizes for the immediate task without awareness of the cumulative structural state. The result is predictable: architecture drift, dependency corruption, and regression cascades.
This condition is referred to as AI Chaos. It is not a consequence of using the wrong tool or writing bad prompts. It is a structural consequence of how prompt-driven development works.
“AI magnifies existing strengths and dysfunctions rather than automatically improving delivery outcomes.”— DORA, 2025 (Google Research, 5,000 respondents)
“Low-quality code contains up to 15× more defects than high-quality code.”— Tornhill & Borg, 2022 (39 proprietary codebases)
Your codebase may be structurally unstable if:
If you recognize three or more of these symptoms, the structural cause is likely measurable.
How the diagnostic works
Repository analysis
Structural dependency graph, file structure, and test infrastructure are analyzed against five root cause dimensions.
Failure pattern detection
Architecture drift, dependency corruption, structural entropy, test infrastructure failure, and deployment safety gaps are identified and scored.
Risk classification
Codebase is classified by the AI Chaos Index (ACI) — a quantitative measure of structural risk from 0 (stable) to 100 (critical).
Diagnostic report delivery
Clear explanation of structural condition, prioritized findings, and recommended next steps. Delivered in 24 hours (Quick Scan) or 2–3 days (Full Audit).
Example diagnostic output
═══════════════════════════════════════════════════ AI CHAOS DIAGNOSTIC REPORT ═══════════════════════════════════════════════════ Repository: client-app (Next.js + Supabase) Generated with: Lovable Age: 4 months | 38k LOC ───────────────────────────────────────────────── ROOT CAUSE ANALYSIS ───────────────────────────────────────────────── RC01 Architecture Drift ........... 7.2 / 10 HIGH RC02 Dependency Corruption ........ 5.8 / 10 ELEVATED RC03 Structural Entropy ........... 4.1 / 10 MODERATE RC04 Test Infrastructure .......... 8.5 / 10 CRITICAL RC05 Deployment Safety ............ 6.3 / 10 HIGH ───────────────────────────────────────────────── AI CHAOS INDEX (ACI) ───────────────────────────────────────────────── ▸ ACI SCORE: 64.8 ─ Risk Band: HIGH ───────────────────────────────────────────────── TOP FINDINGS ───────────────────────────────────────────────── [CRITICAL] 14 files exceed 500 LOC (max: 1,847) [CRITICAL] Test coverage ratio: 3% [HIGH] 6 circular dependency chains detected [HIGH] No CI/CD pipeline [ELEVATED] Business logic in 8 route handlers ───────────────────────────────────────────────── RECOMMENDATION: Structural stabilization recommended before adding features. ═══════════════════════════════════════════════════
This is an example output. Your report will reflect the actual structural state of your repository.
Real findings from recent diagnostics
Every codebase is different. The diagnostic measures yours.
Architecture failure patterns — documented
Structural failure modes, root causes, and detection techniques are documented in the Vibecodiq knowledge base. 21 pages covering the complete AI Chaos landscape.
The path to stability
Diagnose
Architecture Audit
OPENStabilize
ASA Framework Build
POST-AUDITEnforce
Safe-Deploy Layer
POST-AUDITStructural stabilization services are available after diagnostic confirmation. Because structural failures differ significantly between codebases, remediation is performed only after forensic analysis identifies the root causes.
Structural risks compound over time.
Every week without diagnosis is a week where architecture drift, dependency corruption, and regression risk continue to accumulate. The earlier the structural state is measured, the lower the remediation cost.