The Unstructured Enterprise Document Challenge
Over 80% of enterprise operational data remains trapped inside PDFs, scanned vendor contracts, freight bills, and email attachments. Conventional OCR engines extract unformatted text strings, forcing human workers to copy and paste data into ERP forms.
When documents contain rotated pages, low-contrast phone snapshots, or multi-column financial disclosures, traditional regex parsers produce garbled outputs that pollute downstream databases.
Why Traditional OCR Fails Complex Tables and Scans
Legacy optical character recognition treats documents as a sequence of isolated pixels. It has no understanding of reading order, merged table cells, or currency semantics. If a discount is listed in the footer note, legacy tools miss it entirely.
Extracting Verified ERP Entities with Source Citations
DocIntel doesn't just read text; it understands document semantics. It identifies tabular line items, tax breakdown matrices, payment terms, and vendor remittance addresses, linking each extracted field directly back to the visual bounding box on the original PDF.
- Visual Bounding Boxes: Click any number in the ERP invoice to highlight the exact coordinate on the original scanned PDF.
- Currency Normalization: Automatically converts foreign exchange amounts using the transaction date's official bank rates.
- Schema Validation: Ensures line item totals mathematically reconcile to subtotal, tax, and grand total before database insertion.
Instant Semantic Search Across 10,000 Documents
Every processed document is indexed in a vector passage database. Operators can ask conversational questions like "Which vendor contracts have an indemnification cap under $500,000?" and receive instant answers with verified source passage links.