Schedule C tax filing fixed by learning your business from bank data

About 28 million Schedule C filers in the U.S. face a recurring, expensive problem: manual transaction categorization, missed deductions, and inconsistent record-keeping that often forces freelancers, gig workers, and sole proprietors to pay hundreds or thousands for CPA time or to risk audit exposure. These users waste time reconciling bank activity, struggle to translate cash-flow patterns into tax-ready categories, and lack a persistent, trustable source of truth for year‑round tax positions. You could build a $60 ACV subscription product that connects to bank feeds via open-banking APIs, learns each business from transaction history, and produces Schedule C–ready ledgers, categorized deductions, and auditable explanations for every classification; at scale that maps to a $1.7B TAM (28M filers x $60). The technical approach pairs an LLM copilot with persistent context and rules-based reconciliation, plus a user feedback loop and CPA integration to continuously raise categorization precision and generate defensible audit trails; the main execution risks are attaining near‑perfect accuracy, ensuring security/compliance, and building user trust. This is an attractive window: open banking, standardized APIs, and rapid LLM adoption lower onboarding and automation costs, while gig and self-employment growth increase the addressable base—hence the project’s Market Score of 92/100 and Revenue Potential of 90/100 despite medium competition. To stand out, prioritize verifiable accuracy (audit-ready trails and CPA partnerships), a frictionless onboarding experience that learns business context quickly, and product guarantees or audit support to overcome trust barriers; be honest that doing so requires careful data governance, capital for model validation, and deliberate distribution partnerships.