Route docs by image quality to improve OCR & automation outcomes

Many enterprises that run document capture and ID workflows — roughly the 600,000 organizations comprising the target market — still see single-digit to low double-digit percentages of pages fail OCR or break downstream automations because of poor image quality, triggering costly manual review and workflow exceptions. Those interruptions scale with volume: even a 5–25% manual intervention rate on large document streams materially increases labor and delays, eroding the value of existing automation investments. A practical product would be a lightweight, serverless routing microservice that scores image quality per page using modern vision models, applies explainable confidence metrics, and routes each image to the optimal remediation path (best-fit OCR engine, auto-enhancement, re-capture request, or human review). It would expose simple APIs and pre-built connectors to major IDP/OCR providers, support configurable policies and SLAs, and surface operational metrics so teams can tune thresholds and measure ROI; a $30K ACV commercial model aligns with the $18.0B addressable market and typical enterprise procurement cycles. The timing is favorable: recent advances in AI vision make per-image quality scoring reliable enough to drive production routing decisions, enterprise digitization is increasing paper-to-digital volumes, and the shift to composable pipelines/serverless architectures lowers integration friction. Independent market assessments put the market score at 95/100 and revenue potential strong (88/100), which validates pursuing a focused offering rather than a broad platform play. To stand out you should emphasize accuracy, low-latency inference, transparent scoring, privacy/compliance, and turnkey connectors for high-volume verticals (banking, insurance, logistics), while being honest about challenges: calibrating models across document types, maintaining models to avoid drift, and winning initial integrations against medium competition.