Document Processing

Automate extraction and validation of data from documents to improve speed and accuracy.

Business impact

• processing speed — AI accelerates document data extraction and classification, reducing cycle times
• accuracy — AI minimizes human errors by automating data extraction and validation
• operational efficiency — Streamlined workflows reduce manual labor and improve throughput
• cost reduction — Automation lowers labor costs and error-related losses
• compliance adherence — Consistent data extraction supports regulatory and audit requirements

Data requirements

• Scanned documents and PDFs (Image) — Primary input for OCR and data extraction processes
• Structured databases (Structured) — Used for grounding and validating extracted data
• Text from documents (Text) — Processed by NLP models for classification and extraction
• Handwritten notes (Image) — Processed via handwriting recognition to digitize data
• Metadata and logs (Structured) — Used for audit trails and exception management

AI methods and techniques

• Predictive AI — Predicts document types and extracts relevant fields with high confidence
• Generative AI — Generates structured outputs and assists in data validation and error correction
• Agentic AI — Automates multi-step document workflows with configurable governance
• Symbolic AI — Applies rule-based validation and compliance checks on extracted data

AI models and model families

Claude Opus, OpenAI Codex, Llama Nemotron Nano VL, Transformer-based QET, Domain-specific language models

Industries

Real-world evidence

11 documented case studies on record.

Companies using this: Docusign, Edison Scientific, Ibml, Justt, Nutrient, Paytm, Quantiphi, Salesforce, Sema4, The Hartford, Upstage.