Stop manual receipt entry — photo-to-spreadsheet expense capture

Executive Summary

Many small businesses and freelancers—roughly 400 million globally—still rely on manual receipt entry, which consumes time, introduces transcription errors, and leads to missed deductions and delayed bookkeeping. This burden is especially acute for sole proprietors and micro‑SMBs without dedicated finance staff, where even a few hours a month of manual work materially affects margins. The product would be a mobile-first photo-to-spreadsheet expense capture service that combines modern OCR and AI parsing to extract vendor, date, tax, line items, and amounts, then writes validated rows directly into Google Sheets/Drive and exports to major accounting systems. Core features would include continuous model retraining for accuracy, confidence thresholds with lightweight human review, deduplication, configurable categorization rules, offline capture, and a basic $50/year tier plus higher-value automation offerings for bookkeeping partners. This is a timely opportunity: ML/OCR accuracy has improved enough to meaningfully reduce friction across varied receipt formats, platform-first integrations match user preferences, and ongoing SMB digitization supports adoption — together implying a roughly $20B addressable market at $50 ARPU. To differentiate from medium competition we would focus on measurable extraction accuracy targets (e.g., >95% on key fields for common receipts), seamless Sheets/Drive‑first workflows, and distribution through accounting partners and app marketplaces. Real challenges remain: achieving and maintaining high global parsing accuracy, scaling distribution to reach the long tail of SMBs, and navigating privacy/compliance tradeoffs, but the market score (88/100) and revenue potential (84/100) suggest the opportunity is worth exploring with disciplined engineering and partnership execution.