Production-grade document AI for unstructured PDFs, contracts, claims, and medical records. Parse → Classify → Extract → Validate → Score → Graph → Link → Output. Sub-2s per 10-page doc.
Across 50+ projects delivered, these are the patterns that kill document AI in production. Every stage of our pipeline exists to neutralize one of them.
Analysts copy fields from PDFs into Epic, Guidewire, or Salesforce by hand
Off-the-shelf OCR misses values in tables, stamped fields, and multi-column layouts
A naive GPT-4o prompt invents plausible-looking dates, account IDs, and dollar amounts
Today's pipeline extracts one PDF at a time with no awareness of prior documents
A vendor updates a form template and precision drops from 98% to 71% overnight
Analysts copy fields from PDFs into Epic, Guidewire, or Salesforce by hand
22 hours/week per analyst lost to keystroke work no human should be doing
$60-90K/year per FTE doing data entry that an extraction pipeline handles in seconds
How we neutralize it: Every stage of the 8-step pipeline below is designed to catch this failure mode before it reaches your system of record.
No black-box SaaS. Every component is named, swappable, and runs in your VPC.
PyMuPDF for native text, Unstructured.io for semi-structured layouts, Azure Document Intelligence / AWS Textract / Google Document AI for scanned and complex forms. The router picks the right tool per page.
DistilBERT fine-tuned on your document taxonomy routes each file to the correct extraction schema. Contract vs. invoice vs. medical record vs. FNOL packet — decided in milliseconds before extraction runs.
GPT-4o pulls fields with high recall; regex and Pydantic schemas enforce shape; Anthropic Claude runs a second-pass critic on the JSON. Hallucinations cannot escape the validation gate.
Cross-field consistency rules (date ranges, sum checks, foreign-key lookups) plus per-field confidence scores. Low-confidence fields auto-route to a human-in-the-loop queue rather than silently entering the system of record.
Extracted entities are deduplicated through Neo4j (or pgvector similarity) and linked across documents. Same claimant, same matter, same patient — recognized and joined, not re-extracted.
Every deployment ships with a labeled eval set, CI gates, and weekly drift monitoring. When precision drops on a critical field, the pipeline blocks the release and routes docs into review.
Each stage is a named component with its own SLA, its own eval set, and its own owner. You can swap any one of them without rewriting the rest.
PyMuPDF · Unstructured.io · Azure Document Intelligence
Router selects the right parser per page. Native PDFs go through PyMuPDF; scanned, stamped, or table-heavy pages route to Azure Document Intelligence, AWS Textract, or Google Document AI.
DistilBERT (fine-tuned on your taxonomy)
A lightweight classifier decides what kind of document this is — contract, invoice, medical record, FNOL packet, deposition. The decision controls which extraction schema runs next.
GPT-4o · Regex · Pydantic schemas · LayoutLMv3
LLM pulls fields with high recall; regex captures deterministic patterns (NPIs, claim IDs, ISO dates); LayoutLMv3 handles spatially-aware extraction on forms; Pydantic enforces shape and type.
Rules engine · Anthropic Claude critic · cross-field checks
Anthropic Claude runs a second-pass review on the extracted JSON. Cross-field rules check sums, date ranges, and foreign-key lookups against your source systems.
Confidence calibration · per-field thresholds
Each field gets a calibrated confidence score. Fields below threshold route to a human-in-the-loop queue; high-confidence fields proceed to the graph and downstream systems.
Neo4j · pgvector entity store · Pinecone / Qdrant
Entities (people, organizations, accounts, claims, contracts) are deduplicated and stored as nodes. Embeddings live in Pinecone or Qdrant for similarity recall on free-text fields.
Cross-doc reference resolution
New documents are linked into the existing graph. Same claimant across multiple FNOL packets, same matter across deposition + contract + amendment — joined, not re-extracted.
Structured JSON · REST · webhooks · system-of-record sync
Final payload is shaped to your downstream contract: Epic Bridges HL7 / FHIR, Guidewire ClaimCenter PolicyCenter, Salesforce, SAP S/4HANA, NetDocuments, iManage, or raw JSON over webhook.
The same 8-stage pipeline, configured per domain. Schemas, integrations, and compliance posture change — the architecture does not.
Discharge summaries, op notes, and lab reports parsed into FHIR resources. PHI de-identification runs at the Parse stage; the pipeline ships in BAA-covered Azure tenants or fully on-prem with Llama 3 + LayoutLMv3.
First Notice of Loss packets — police reports, repair estimates, photos, ACORD forms — extracted into structured claim records and pushed straight into Guidewire. Adjusters get a populated claim, not a stack of PDFs.
MSAs, NDAs, vendor agreements abstracted to a structured schema and linked to the matter in your DMS. Cross-document references — amendments, exhibits, side letters — resolved into a single contract knowledge graph.
K-1s, fund subscription docs, capital call notices, and quarterly statements parsed into a multi-family-office accounting system. Entity resolution links the same LP across funds, vintages, and reporting periods.
Citizen-facing form submissions, FOIA request packets, and case files routed to the right downstream system with confidence-scored fields. On-prem deployment available for air-gapped environments.
Measured on customer corpora, not on academic benchmarks.
Every milestone has a deliverable you can grade. No black-box phases, no rolling deadlines.
We sample 50-100 of your real documents, design Pydantic schemas per doc type, and define the eval set with you. Outputs: schema spec, eval set, integration contract.
Working end-to-end pipeline on your sample corpus. Streamlit eval harness so your team can grade every field. Baseline precision and recall measured per schema.
Pipeline deployed into your VPC (AWS / Azure / GCP / on-prem). Integration into Epic, Guidewire, Salesforce, or your DMS. Drift gates and CI eval running on every release.
Runbooks, on-call coverage, weekly drift reports. Your team owns the schemas; we own the pipeline reliability until you choose to take it in-house.
The questions our customers ask before they sign — and what we tell them.
Send 50-100 of your real PDFs. Two weeks later, you will have a working prototype, a precision/recall report, and a clear path to production.
Backed by 50+ projects delivered, 96% client satisfaction, 4 production AI products