Building Agentic Due Diligence for High-Ticket Marketers: Architecture, Risks and Open Questions
AgentVN · reddit · 2026-09-06
The author proposes an automated due-diligence app: users input a company, person, domain, social profile, or landing page, and a network of specialized AI agents investigates high-ticket course sellers, agencies, and aggressive e-commerce brands, producing an evidence-backed structured report.
Key design points:
- Not a "scam detector": only verifiable facts and inconsistencies (e.g., domain registered 4 months ago while the site claims 9 years in business), never a "scammer" label, to avoid legal risk
- Entity resolution first: linking people, companies, domains, ad pages, and old business names — searching one brand often misses dissolved LLCs and complaints filed under a founder's legal name
- Testimonial research: detecting identical testimonial copy across unrelated sites, suspiciously timed review accounts, repeated writing patterns, the same "customer" appearing for multiple brands, AI avatars and synthetic voices, and more
Every finding includes screenshots and sources, with similarity flagged as possible matches with confidence levels. The author seeks feedback on architecture, false positives, legal risk, and whether this is truly an agentic use case or better built as a deterministic pipeline with selective LLM analysis.
Related event: Developer Proposes Multi-Agent AI Tool for Vendor Due Diligence(2 posts)→
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