How accurate is automated document extraction?

For standard formats like invoices, 90-98% accuracy is typical. For unstructured documents, 80-95% depending on document quality. We always include human-in-the-loop review for low-confidence extractions.

Can the system learn from corrections?

Yes. We implement active learning where human corrections are fed back to improve the model. Accuracy improves over time as the system processes more documents.

What document formats are supported?

PDF, TIFF, JPEG, PNG, Word, and Excel. We handle both digital-native documents and scanned paper documents with varying quality.

Intelligent Document Processing at Scale

OCR, NLP extraction, classification, and automated workflows. Our AI-managed teams build document processing pipelines that turn unstructured documents into structured data.

Solution: Document Processing

Intelligent document processing (IDP) combines OCR, natural language processing, and machine learning to extract structured data from unstructured documents — invoices, contracts, forms, receipts, and reports. A production IDP pipeline handles ingestion, classification, extraction, validation, and downstream integration.

Stack Components

Tesseract / AWS Textract (OCR Engine): Optical character recognition that converts scanned documents and images into machine-readable text with layout preservation.
spaCy / Hugging Face (NLP & Entity Extraction): Named entity recognition, key-value extraction, and document classification using pre-trained and fine-tuned language models.
Python / FastAPI (Processing Backend): High-performance Python backend for orchestrating document processing pipelines with async processing and queue management.
PostgreSQL / Elasticsearch (Storage & Search): Structured data storage for extracted fields with full-text search across processed documents.
Celery / AWS Step Functions (Pipeline Orchestration): Manages multi-step document processing workflows with retry logic, error handling, and progress tracking.

Best For

Invoice and receipt processing
Contract analysis and extraction
Medical record digitization
Insurance claim processing
Legal document review automation
KYC document verification

Case Studies

Invoice Processing Automation: Automated invoice processing system extracting vendor, amount, line items, and payment terms from 10,000+ invoices monthly for an accounts payable department.
- 95% extraction accuracy on standard invoice formats
- Processing time reduced from 5 minutes to 15 seconds per invoice
- Automated matching against purchase orders
- 80% reduction in manual data entry labor
Legal Contract Review: Document processing pipeline that extracts key clauses, dates, parties, and obligations from legal contracts for a corporate legal team.
- NLP extraction of 20+ clause types from contracts
- Risk scoring based on unfavorable terms
- Searchable contract database with clause-level indexing

Frequently Asked Questions

How accurate is automated document extraction?: For standard formats like invoices, 90-98% accuracy is typical. For unstructured documents, 80-95% depending on document quality. We always include human-in-the-loop review for low-confidence extractions.
Can the system learn from corrections?: Yes. We implement active learning where human corrections are fed back to improve the model. Accuracy improves over time as the system processes more documents.
What document formats are supported?: PDF, TIFF, JPEG, PNG, Word, and Excel. We handle both digital-native documents and scanned paper documents with varying quality.