Document Processing & Extraction

Every business drowns in documents — invoices from vendors, contracts from clients, applications from prospects, compliance forms from regulators. Manually reading, interpreting, and entering data from these documents into your business systems is one of the most tedious and error-prone activities any organization endures. Our document processing automations use OCR, computer vision, and large language models to read documents of any format, extract the relevant data points, validate them against business rules, and push structured data into your systems of record.

The processing pipeline handles documents from any input channel. Email attachments are intercepted by monitoring specific inboxes via Gmail or Outlook APIs. File uploads come through web portals, shared drives, or Slack channels. Physical documents enter via scanner integrations or mobile photo capture. Regardless of source, each document passes through an intelligent classification layer that identifies the document type — invoice, contract, W-9, insurance claim, purchase order — and routes it to the appropriate extraction template. This classification uses trained AI models that recognize document layouts, headers, and content patterns.

Extraction accuracy is where our approach outperforms template-based OCR tools. For structured documents like invoices and forms, we combine traditional OCR with layout analysis to identify and extract specific fields: vendor name, invoice number, line items, totals, tax amounts, and due dates. For semi-structured documents like contracts and proposals, we deploy LLMs that understand natural language context — extracting key terms, obligations, dates, parties, and clauses regardless of formatting variations. A contract review automation, for example, can extract renewal dates, termination clauses, payment terms, and liability caps from contracts written in completely different styles.

Post-extraction validation ensures data quality before it enters your systems. Business rules check for reasonable value ranges, required field completeness, cross-field consistency, and duplicate detection. Flagged exceptions are routed to a human review queue with the extracted data pre-populated — the reviewer corrects if needed and approves, training the system to improve over time. Validated data flows into your ERP, accounting software, CRM, or custom database via API integration. We build dashboards that track processing volume, accuracy rates, exception rates, and processing time to demonstrate ROI and identify improvement opportunities.