OCR answers the question “what does this document say?” Underwriting needs a different question answered: “what is this risk?” This page compares generic OCR and document-AI tools with purpose-built insurance submission ingestion.
| Generic OCR / document AI | SnapLine | |
|---|---|---|
| Output | Raw text or generic key-value pairs | Typed insurance fields: TIV, BI, occupancy, construction |
| MRC slips | Structure lost, clauses jumbled | Native understanding of Market Reform Contract structure |
| Schedules of values | Table extraction breaks on real-world files | Line-level extraction across hundreds of locations |
| Multi-document packs | Each file processed in isolation | Cross-document reconciliation and conflict flags |
| Enrichment | None | Hazards, valuation adequacy, vacancy per location |
| Insurance QA | Generic confidence only | Domain validation with ongoing QC checks |
OCR is a component, not a solution. The distance between “text extracted” and “quote-ready submission” is where underwriting teams actually spend their time — reconciling documents, validating values, looking up risk data. SnapLine covers that distance; OCR stops at the first step.
No. Generic tools produce text; SnapLine produces underwriting data and risk intelligence. Many teams discover their OCR output still requires the same manual work it was meant to remove.
Yes — scans, images and mixed-quality PDFs are part of normal ingestion, with confidence scoring reflecting document quality.
SnapLine vs Manual Processing · Submission Ingestion vs IDP · Intelligence vs Extraction
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