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Document Intelligence
Extract data from any document, any format
Capabilities
Process PDFs, scanned images, photographs, faxes, emails, and handwritten documents through a unified pipeline. Handle 200+ document types including invoices, purchase orders, contracts, and government forms.
Extract structured data from unstructured documents using transformer-based models. Identify key-value pairs, tables, line items, signatures, and stamps without template configuration for each document type.
Classify incoming documents by type, urgency, and department automatically. Route to appropriate workflows, approval queues, or data systems based on content and extracted metadata.
Validate extracted data against business rules, master databases, and previous submissions. Flag discrepancies, missing fields, and potential errors for human review before committing to downstream systems.
Documents meeting confidence thresholds process end-to-end without human intervention. Typical straight-through rates of 70-85% for standard document types, with continuous improvement through operator feedback.
Use Cases
According to the Institute of Finance and Management, manual invoice processing costs $15-$40 per invoice with average processing times of 10-15 days. The IDP platform extracts vendor details, line items, amounts, tax calculations, and payment terms from invoices in any format — PDF, email, scanned paper, or photographed documents. A 2024 Ardent Partners study found that organizations deploying intelligent document processing reduce invoice processing costs by 80% and cycle times from 10 days to under 3 days. The system validates extracted data against purchase orders and goods receipts, auto-matching three-way where discrepancies fall within configured tolerances. Exception invoices route to AP staff with extracted data pre-populated and discrepancies highlighted, reducing review time from 12 minutes to 90 seconds. Integration with ERP systems including SAP, Oracle, and Tally automatically posts matched invoices for payment, and early payment capture increases discount revenue by 15-25% through consistent adherence to payment terms.
The National Archives of India estimates that government agencies process over 2 billion paper documents annually, with record retrieval times averaging 3-5 days for physical archives. The IDP platform digitizes, classifies, and indexes government records including land records, court filings, permit applications, and citizen certificates at processing speeds of 500 documents per hour per workstation. A 2024 McKinsey Government Digital Transformation study found that AI document processing reduces government record processing backlogs by 75% within the first year of deployment. The system handles Hindi and regional language documents using multi-script OCR with 96% accuracy on printed text and 89% on handwritten documents. Extracted data populates e-governance databases, making records searchable and accessible digitally for the first time. Chain-of-custody tracking ensures document provenance and legal admissibility. Integration with DigiLocker enables direct issuance of verified digital documents to citizens, eliminating the need for certified copies that require physical visits to government offices.
According to the American Health Information Management Association, clinical documentation errors contribute to $36 billion in annual revenue leakage for US hospitals through incorrect coding and denied claims. The IDP platform processes medical records, lab reports, prescription forms, insurance claims, and discharge summaries, extracting clinical data and mapping it to appropriate ICD-10 and CPT codes. A 2025 Journal of the American Medical Informatics Association study found that AI document processing improves coding accuracy by 23% and reduces claim denial rates by 31% compared to manual coding processes. The system handles multi-format clinical inputs including handwritten physician notes, printed lab results, and faxed referral letters. HIPAA-compliant processing ensures patient health information is encrypted throughout the pipeline with role-based access controls. Automated claim pre-validation identifies documentation gaps before submission, reducing the 15-20% denial rate that most hospitals experience and the 60-day average appeal cycle that follows each denial.
Frequently Asked Questions
The platform processes PDFs (native and scanned), JPEG, PNG, TIFF images, Microsoft Office documents (Word, Excel), email attachments (EML, MSG), faxes, and photographed paper documents. It handles single-page and multi-page documents, multi-document PDFs, and ZIP archives containing mixed formats. Document quality as low as 150 DPI is supported with automatic image enhancement.
Yes. The platform includes handwriting recognition (ICR) for both English and Hindi scripts. Accuracy for neat handwriting reaches 92-95%, and for average handwriting 85-89%. For critical handwritten fields like amounts and signatures, the system flags low-confidence extractions for human verification. Handwriting recognition accuracy improves over time as the system learns from operator corrections.
The platform uses zero-shot extraction capabilities that identify common fields (dates, amounts, names, addresses) in unfamiliar document layouts without prior training. For new document types that will be processed repeatedly, a training pipeline creates optimized extraction models from 20-50 sample documents in 3-5 business days. The zero-shot approach handles one-off documents while the trained models provide higher accuracy for recurring document types.
Pre-built connectors exist for SAP, Oracle, Microsoft Dynamics, Tally, QuickBooks, Salesforce, and ServiceNow. Integration with other systems uses REST APIs, SFTP file exchange, or RPA bots that populate fields in legacy applications. Most ERP integrations are operational within 2-3 weeks of deployment, running in parallel with existing processes during validation.
The platform applies multi-layer validation: format checks (dates, amounts, identifiers), cross-field logic (line items summing to total), master data matching (vendor names, product codes against databases), and confidence scoring for each extracted field. Fields below confidence thresholds route to human operators who see the original document alongside extracted data for quick verification. Validation rules are configurable per document type.
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