Extract Data from Documents with AI Precision

We process virtually any document type: Invoices, receipts, purchase orders, bank statements (Financial). Contracts, agreements, legal notices, court documents (Legal). Resumes, applications, forms, timesheets (HR). Patient records, lab reports, prescriptions, insurance claims (Healthcare). Passports, IDs, licenses, utility bills (Identity/KYC). Bills of lading, packing slips, customs forms (Logistics). Any PDF, scanned image, photograph, or digital document. We handle structured documents (forms, invoices), semi-structured (emails, reports), and unstructured (contracts, notes). Documents can be multi-page, multi-format (PDF, JPG, PNG, TIFF), multi-language, handwritten or printed, and in any quality from high-resolution scans to mobile phone photos.

Accuracy varies by document type and quality: Structured forms (invoices, standardized forms): 95-99% accuracy. Semi-structured documents (contracts, reports): 90-95% accuracy. Handwritten documents: 85-95% depending on legibility. Low-quality scans/photos: 85-90% with preprocessing. Factors affecting accuracy include document quality and resolution, consistency of layout, language and fonts, handwriting legibility, and training data volume. We improve accuracy through custom model training on your documents, human-in-the-loop validation for uncertain extractions, confidence scoring to flag low-confidence fields, continuous model refinement based on corrections, and preprocessing (image enhancement, rotation, noise removal). Most clients achieve 95%+ accuracy within 2-3 months of deployment as models learn from corrections.

Implementation timeline depends on complexity: Simple projects (single document type, standard format): 3-4 weeks from kickoff to production. Medium complexity (multiple document types, some variability): 6-8 weeks including model training and testing. Complex projects (many document types, high variability, custom requirements): 10-12 weeks with extensive training. Breakdown: Week 1: Document analysis and requirements. Weeks 2-4: Model training and initial testing. Weeks 4-6: Integration development and refinement. Weeks 6-8: UAT and deployment preparation. Week 8+: Production deployment and support. We can show value quickly with a 2-week pilot processing one document type to validate approach and demonstrate ROI before full implementation.

Typical ROI includes: 70-90% time savings on document processing tasks, 60-80% labor cost reduction (e.g., data entry staff), 90%+ reduction in data entry errors, and payback period of 6-12 months. Example calculation: A company processing 5,000 invoices/month at 5 minutes each (417 hours) at $25/hour costs $10,417/month. With automation reducing to 30 seconds per invoice (42 hours), costs drop to $1,042/month plus $2,000 for automation platform = $3,042 total. Monthly savings: $7,375 ($88,500 annually). With $60,000 implementation cost, ROI achieved in 8 months. Additional benefits include faster processing enabling early payment discounts, improved cash flow visibility, better vendor relationships, audit trails for compliance, and freed resources for strategic work.

We use multiple techniques for challenging documents: Preprocessing (image enhancement to improve clarity, deskewing and rotation correction, noise removal, binarization for better contrast, resolution upscaling). Advanced OCR (multiple OCR engines for comparison, ensemble methods combining results, deep learning OCR for complex layouts, handwriting-specific models). Contextual Understanding (NLP to understand context, entity relationships, business rules validation, cross-field validation). Human-in-the-Loop (confidence scoring to flag uncertain extractions, review queues for manual verification, active learning from corrections, exception handling workflows). Fallback Processes (manual data entry for very poor quality, partial automation with human completion, quality thresholds for automation vs manual). Most documents improve with preprocessing; very complex or poor quality documents may require manual review, but we still automate workflow routing and validation.

Yes, but with some limitations: Modern AI models can recognize handwriting with 85-95% accuracy for legible handwriting, structured forms (boxes for characters), common languages (English, numbers), and printed-style handwriting. Accuracy is lower for cursive writing, very messy handwriting, unusual writing styles, and uncommon languages. Our approach for handwritten documents: Use specialized handwriting recognition models, combine multiple OCR engines, implement character-level recognition, use context and business rules for validation, and employ human-in-the-loop for uncertain characters. Best results with forms where handwriting fills specific fields, check boxes and signatures, dates and numeric values, and standardized answer formats. For critical handwritten data, we recommend human verification of AI extractions to ensure accuracy while still benefiting from automation in routing and workflow.

Security and compliance are built into our document processing: Data Security (encryption at rest and in transit (AES-256, TLS 1.3), secure document storage with access controls, automatic PII/PHI detection and masking, role-based access to extracted data, audit logs of all document access). Compliance Frameworks (GDPR compliance with right to deletion, HIPAA for healthcare documents, SOC 2 Type II certified processes, PCI-DSS for financial documents, industry-specific regulations). Processing Options (on-premise deployment for sensitive data, private cloud instances, document retention policies, automatic purging after processing, air-gapped environments available). Data Handling (no document storage beyond necessary retention, secure credential management, data anonymization for model training, vendor agreements with strict terms). All processing follows your data governance policies and can be audited for compliance verification.

Yes, we integrate with virtually any system: Integration Methods (REST APIs for real-time extraction, batch processing for bulk documents, webhook callbacks for async processing, file-based integration (watch folders), direct database connections, message queues (Kafka, RabbitMQ)). Common Integrations (ERP systems (SAP, Oracle, NetSuite), Accounting software (QuickBooks, Xero), Document management (SharePoint, DocuSign), CRM systems (Salesforce, Dynamics), Workflow tools (ServiceNow, Jira), Email systems (Office 365, Gmail), Cloud storage (S3, Azure Blob, Google Drive)). Integration Patterns (documents uploaded via API or email, automatic extraction and validation, results posted to target system, notifications on completion or errors, human review queue for exceptions, audit trail and reporting). We provide SDKs and connectors for common platforms, or custom integrations for proprietary systems.

Variations and exceptions are expected in real-world documents. Our approach: Template Matching (identify document layout variations, match to trained templates, handle multi-page documents, process different invoice formats). Adaptive Learning (models learn from new document variations, continuous training with feedback, transfer learning for similar document types, version control for model updates). Exception Handling (confidence scoring for every extraction, automatic flagging of low-confidence fields, business rule violations trigger review, missing required fields escalated, unusual values validated). Human Review Workflow (review queue for exceptions, side-by-side document and data view, quick correction and approval, corrections fed back to model training, analytics on exception types). Continuous Improvement (track exception categories, identify patterns, create new templates, refine extraction rules, expand model coverage). Most systems achieve 90%+ straight-through processing after initial training period.

Document processing requires ongoing support for optimal performance: Model Maintenance (retrain models with new document types, fine-tune for improved accuracy, update for layout changes, add new languages or fields, quarterly model performance reviews). System Monitoring (extraction accuracy tracking, processing time metrics, error rate monitoring, capacity utilization, SLA compliance). Issue Resolution (investigate failed extractions, fix integration issues, resolve performance problems, update validation rules, 24/7 support for critical systems). Continuous Improvement (analyze exception patterns, optimize extraction rules, add new document types, improve confidence scoring, user feedback incorporation). Our Support Tiers: Basic (business hours support, monthly model updates), Standard (24/7 monitoring, weekly model tuning, 4-hour response), Premium (dedicated support team, daily optimization, 1-hour response, proactive improvements). Most clients start with Standard support and adjust based on volume and criticality.