Automated legal-appeal tool for medical debt & tenant harms

Millions of low-income consumers face medical debt disputes and tenant harms—billing errors, aggressive collection, eviction filings—often without legal representation; roughly 50 million vulnerable U.S. households could be exposed to these harms. These people routinely miss filing windows, fail to produce the narrowly required documentation, and cannot craft legally sufficient appeals, producing avoidable judgments, garnishments, and housing loss. An automated legal-appeal tool would intake bills, notices, and court documents, extract facts, generate state- and issue-specific appeal letters and motion drafts, and route matters for human review, monetized through subscriptions, paid concierge services, referrals, and contracts with clinics and community organizations; the back-of-envelope TAM is about $20.0B assuming a $400 multi-year LTV across 50M households. Practically this requires coupling LLM-driven drafting with deterministic rule engines, document OCR and classification, and workflows that produce auditable outputs suitable for legal aid partners. The market is attractive now because LLM accuracy is improving, funders are prioritizing scale solutions for access to justice, and more courts and billing systems produce machine-readable records; independent scoring here is high (market score 92/100, revenue potential 88/100) and competition is currently low. Major challenges are real: model hallucinations, state‑by‑state legal variance, privacy and regulatory risk, and the need to build trust with clinics and courts; defensible differentiation will come from rigorous validation studies, transparent and auditable reasoning, state rule libraries, and formal partnerships with legal aid organizations rather than relying solely on consumer-facing promises.