OCR, page classification, structured extraction, validation agents and grounded RAG — turning PDFs, scans and forms into trustworthy structured data. Every field traces back to its source page, with humans in the loop where it counts.
Teams re-key fields from invoices, menus, batch records and clinical files — slow, expensive and error-prone at volume.
Poor scans, mixed layouts and thousand-page bundles defeat naive OCR and break downstream automation.
Ungrounded models invent values you can't audit — unacceptable for finance, pharma and healthcare.
From raw upload to validated, structured output — engineered, queued and observable.
Accept PDFs, images, ZIPs and Excel; split, route and classify every page (OCR / vision-LLM / adaptive) before extraction — the foundation for clean, reliable output.
Azure Document Intelligence, Landing AI ADE and vision-LLMs extract fields, tables and entities into your target schema — hundreds of fields auto-filled, zero re-keying.
Tens of validation agents check extracted data against rules and source, surfacing only exceptions — the pattern behind GMP batch-record review at scale.
Ask questions across thousand-page bundles with answers grounded in the source — PDF-level traceability, zero-hallucination retrieval over scanned financials and contracts.
Editor UIs, confidence flags and golden-set regression testing keep a person on the hard cases — accuracy you can sign off on, improving over time.
OpenAI, GLM, DeepSeek and open models behind one pipeline, with per-job cost, duration and success-rate metrics — quality and spend under control.
Grounded RAG with OCR answering questions over 2,000+ page scanned bond documents — zero hallucination, PDF-level traceability.
Read the case study →Digitizes physical menus into structured, import-ready data via a 3-stage pipeline — 200+ fields auto-filled, zero manual entry.
Read the case study →A GMP-compliant platform with 50+ validation agents — 60–80% less review time across 300+ products.
Read the case study →AI review-by-exception for batch records — 21 CFR Part 11 compliant, ~70% less manual review time across sites.
Read the case study →Ingests entire hospitalization records, classifies every page, and synthesizes a clinician-ready structured discharge summary.
Read the case study →Document processing, GPT-4o counselling and video-interview scoring cut vetting and response delays across the funnel.
Read the case study →Every field links back to its source page
Retrieval anchored to documents — no invented values
21 CFR Part 11 / GMP-ready audit trails
Azure, AWS or open stacks — your data stays yours
It turns PDFs, scans and forms into trustworthy structured data through OCR, page classification, structured extraction, validation agents and grounded RAG — with every field traceable to its source page.
Azure Document Intelligence, Landing AI ADE and vision-LLMs, orchestrated with multi-provider routing across OpenAI, GLM, DeepSeek and open models, with cost and quality observability.
Retrieval is grounded in the source documents, validation and review-by-exception agents check every field, and confidence flags route hard cases to human review — with PDF-level traceability.
Yes — it powers 21 CFR Part 11 and GMP batch-record review and clinical documentation, with audit trails and human-in-the-loop sign-off.
Tell us the document and the outcome — we'll bring the engineers who've shipped extraction and document intelligence in pharma, finance and healthcare.