The challenge: ESG platforms need: - Deterministic calculations (auditors reject "AI math") - Immutable audit trails (SOX/SOC2 requirements) - Multi-jurisdictional compliance (EU CSRD, India BRSR, US SEC) - Real-time anomaly detection + AI document generation

*My experiment:* I used Cursor, GitHub Copilot, and Amazon Q (Kiro) to architect the entire stack, guided by a structured "WAR-MODE" prompt covering: 1. Technical architecture (multi-region, event sourcing, circuit breakers) 2. ESG methodology (GHG Protocol validators, uncertainty quantification) 3. Regulatory engines (BRSR/CSRD/SEC automation) 4. Product/UX (role-based onboarding, supplier agent, no-code workflows)

*AI correctly identified:* "Never use LLMs for emission calculations—auditors will reject it" "Implement WORM storage for audit trails, not 'agent memory'" "Multi-model strategy: GPT-4V for OCR, Claude for reports, rules for compliance" "India-first BRSR compliance = competitive moat"

*What I'm unsure about:* - Are there architectural anti-patterns AI tools systematically miss? - For compliance-critical systems, is AI review a complement or substitute for human CTOs? - What's the right balance of AI-generated architecture vs. human validation?

*For experienced CTOs/architects:* What would you want to validate in a system like this that AI likely couldn't catch? And conversely, are there areas where AI review is now legitimately superior to human review (e.g., exhaustive checklist coverage)?

I'm happy to share: - The full WAR-MODE prompt structure (so you can adapt it) - Our architecture decisions and trade-offs - Specific gaps we're worried about

Curious to hear from folks building audit-grade or compliance-heavy systems.