Client has never filed corporate tax returns from 2018 to 2025 and is missing some historical bank statements, requiring a manual, risky reconstruction process.

The current competitive landscape for tax preparation and accounting software is dominated by established players like Intuit (ProSeries, Lacerte), Thomson Reuters (UltraTax), and Drake Software. These products excel when clients have clean, complete records. However, they are fundamentally designed for annual filing with well-organized data, not for reconstructing multiple years of unfiled returns from fragmented records. Standalone trial balance tools (e.g., CaseWare, GoSystem Tax RS) exist but require manual data entry and offer no AI-powered gap-filling. Excel workbooks remain the default for many CPAs, leading to error-prone, non-auditable processes. The gap is clear: no product specializes in ingesting messy, incomplete data (scans, partial statements) and generating defensible trial balances and tax filings year by year. A new entrant can exploit this by providing an end-to-end reconstruction workflow with AI estimation, audit trails, and direct tax return outputs—something no current tool offers.

The ideal customer is a tax partner or manager at a mid-sized CPA firm (10–50 CPAs) who regularly accepts clients with poor recordkeeping and multiple unfiled years. These clients are typically small business owners in construction, retail, or hospitality who have been operating for years without corporate tax compliance. The buyer is the firm owner or partner, while the primary users are staff CPAs and tax preparers. Their current workflow involves manually requesting bank records from the IRS or banks, poring over shoeboxes of receipts, and using trial balance templates in Excel to reconstruct years one by one—a process that takes 20–40 hours per client and carries high professional liability. They are triggered to look for a solution when a new client with 5+ unfiled years walks in, or when a partner realizes the risk and inefficiency of manual reconstruction. Budget is typically $150–$300 per client engagement or $150–$250/month per user for tools that demonstrably save time and reduce risk. Willingness to pay is explicit at the $199/mo unlimited or $99/return price points.

TaxTime Reconstruct should differentiate by becoming the first purpose-built reconstruction platform for CPAs. The core differentiator is an AI engine that not only categorizes receipts and infers missing transactions but generates a fully documented audit trail for every estimate—citing basis, similar periods, and industry averages. This directly addresses the primary fear of CPAs: audit risk from using unsupported estimates. Positioning should emphasize 'Audit-Ready Reconstruction in Hours, Not Weeks.' A second angle is pricing: the $99/return model aligns with per-client billing and makes adoption easy for firms wary of subscriptions. The platform should also feature a 'gap map' visual that shows which months have data and which were estimated, allowing CPAs to review and override AI decisions. Vertical integration with tax filing (e.g., exporting to IRS e-file or PDF forms) would complete the workflow. A niche focus on multi-year reconstruction avoids head-on competition with full-suite tax software and builds a defensible moat.

Technical risk is medium-high. Building AI that reliably estimates missing financial data from fragmented sources (scanned receipts with varying quality, partial bank statements) is non-trivial. The model must handle edge cases like missing entire years versus missing only a few months, and estimates must be both reasonable and defensible under IRS scrutiny. Market risk is elevated due to the low signal count (only 1 independent report), though high severity (4/5) suggests a genuine pain point. The niche is small, so customer acquisition costs may be high through traditional channels. Execution risk includes convincing conservative CPAs to trust AI-generated estimates and overcoming their preference for manual control. Regulatory risk is significant: the IRS has strict guidelines for estimates and reconstructions. The platform must ensure that every estimated entry complies with Revenue Procedures (e.g., Rev. Proc. 2009-38 for reconstruction of accounting records) and clearly documents the methodology. Overall, risk is medium-high, but the combination of AI automation and audit trail could create a strong value proposition if built with regulatory advisors from the start.

1. Post on r/taxpros and r/accounting asking: 'How do you handle a client with 5+ years of unfiled corporate tax returns and missing bank statements? Would you use a tool that auto-generates trial balances with documented estimates?' and gauge response. 2. Build a landing page with a video demo of the AI gap-filling feature, offering early access at $79/return. Run Facebook/LinkedIn ads targeting CPAs (role: 'Tax Manager' or 'Partner'). Measure sign-ups and clicks. 3. Interview 5–10 CPAs from small firms (<20 staff) who have encountered such clients. Ask for the current manual process, time spent, and willingness to pay $199/mo. Record specific objections to AI estimates. 4. Create a clickable prototype (e.g., in Figma or Retool) showing the 'gap map' and estimate justification. Show it to the same CPAs and ask if it reduces audit anxiety. 5. Analyze competitor gaps by purchasing a demo of Drake or UltraTax and testing how they handle incomplete data. Document specific pain points. 6. Test pricing via a simple A/B test on the landing page: half see $199/mo unlimited, half see $99/return. Track conversion (email capture). 7. Consult a tax attorney or enrolled agent to ensure the AI's estimate documentation meets IRS audit defense standards before building a full MVP.

Directional estimate: The total addressable market (TAM) includes all U.S. CPA firms that handle corporate tax returns (approx. 40,000 firms) multiplied by the average number of clients with unfiled years per firm (estimated at 1–2 per firm based on industry anecdotes) and an average fee of $500–$1,000 per return. This yields an annual TAM of $40 million to $80 million for the reconstruction service alone. The serviceable addressable market (SAM) is smaller, focusing on the ~5,000 firms that actively market to and serve chronically non-compliant small businesses. The serviceable obtainable market (SOM) for a new entrant with limited marketing might be 500–1,000 firms in the first 2–3 years, generating $1–$2 million in annual revenue at $199/mo or $99/return pricing. This sizing is uncertain due to the minimal signal count, but high severity suggests a niche that could sustain a small, profitable SaaS product. Pricing sensitivity and actual adoption rates would need further validation through the steps above.