Go from rule-based OCR to LLM-led document workflows

DocDhara is an enterprise IDP suite built on agentic models. Rather than relying on simple templates or rule-based OCR, DocDhara combines high-resolution geometry parsing with private LLMs to classify layout contexts and extract structured fields with 99%+ accuracy.

Rule-based OCR breaks when columns shift, layout sizes change, or text is handwritten. DocDhara uses GenAI layout fusion models to read document templates semantically. It reads values based on meaning and visual context rather than rigid coordinate bounding boxes.

DocDhara is built for strict enterprise compliance. It deploys inside your AWS, GCP, or Azure Virtual Private Cloud (VPC). No document payloads, text snippets, or extracted metadata fields are ever sent to external servers. You control the encryption keys and logs.

Yes. DocDhara excels at extracting data from highly unstructured documents like bank statements, ACORD insurance forms, tax declarations, utility bills, and legal contracts. It handles tables with split rows, multi-page lists, checkboxes, and handwriting.

If our Extraction Agent reads a field with confidence below your configured threshold (e.g. 95%), the document is automatically routed to the validation interface. Your human reviewer corrects it, and the correction is captured in database audit logs to improve future model updates.

DocDhara outputs standard JSON or XML payloads that sync directly via webhooks or private API endpoints to NetSuite, SAP, Salesforce, Epic, Guidewire, and PostgreSQL/Supabase databases.

Yes. Using the GyanDhara annotation tool, you can upload as few as 10-15 sample documents, tag the target key nodes, and deploy a custom extraction model immediately. Model weights remain fully inside your private secure boundaries.

You can click 'Start Free Trial' to request a 14-day playground instance. For enterprise teams, we set up a custom proof-of-concept (POC) running directly within your private cloud environment to validate security and extraction accuracy metrics.