Intelligent document processing services, AI document processing, and computer vision services. We build custom multi-modal IDP pipelines for invoices, contracts, claims, KYC bundles, medical records, and logistics docs. Claude Opus 4.7 vision, GPT-5 vision, or open VLMs picked per doc-type, with confidence routing, HITL queues, and downstream integration into NetSuite, SAP, and Workday. First pipeline live in 30 days. Per-field accuracy reported with confidence bands.
Intelligent document processing (IDP) is the practice of extracting structured data from semi-structured documents (invoices, claims, contracts, MSAs, KYC forms) using vision-capable language models that read context, not just characters. Unlike traditional OCR (Tesseract, AWS Textract baseline) which converts pixels to text without understanding which text is a vendor name or an invoice total, IDP outputs structured JSON keyed to the business fields the downstream system expects. Unlike rule-based extraction (regex on text-only OCR), IDP handles layout variation, table extraction, multi-page joins, and language switching without rewriting rules. Common stacks pair GPT-5 vision or Claude Sonnet 4.6 vision with Unstructured.io or Azure Document Intelligence for layout preprocessing, attach a per-field confidence score, route fields below 0.85 confidence to a human reviewer, and persist every extraction with its source document hash for audit.
Classic intelligent document processing meant OCR plus rules plus per-template training. Modern ai document extraction uses a multi-modal vision-language model that classifies the document, extracts fields into a strict schema, validates the result, and routes by confidence. The category name has not changed; the engineering inside it has. The doc-types below are the patterns we ship most often.
ai invoice extraction with line-item, tax-code, and PO-match logic — AP-grade, eval-tested, idempotent. We push to NetSuite, SAP, QuickBooks, Bill.com with a 3-way match before posting. Common edge cases we handle: multi-page line items, foreign-currency invoices, handwritten approval stamps, and the dreaded multi-supplier consolidated bill.
AI contract review and AI contract analysis: clause extraction, obligation tracking, renewal-date detection, and risk-flag routing. We anchor against your playbook (MSAs, NDAs, DPAs, SOWs) and only auto-extract on clauses that match your eval-set; everything else routes to the contracts team with a draft summary attached.
FNOL intake bundles, medical records, police reports, repair estimates: multi-document classification, completeness checks, and claim-bundle routing to the right adjuster queue. Honest scope: we triage and assemble. We do not make liability decisions. That stays human until your compliance team says otherwise.
KYC automation across passport, driver's-licence, utility-bill, and corporate-registry document bundles. Liveness-check integration on request. We extract, cross-validate, and route to your sanctions and PEP screening. We do NOT replace the screening engine itself.
Patient-intake forms, lab reports, prior-authorization documents, claim forms. HIPAA-aware engagements with BAA-covered model choices (Claude via Bedrock, Azure OpenAI with retention disabled, or self-hosted open VLMs). Cross-link target: see `/industries/healthcare/` for the broader healthcare AI engagement model.
Bills of lading, packing lists, customs declarations, certificates of origin. Heavy on stamps, handwriting, and unstructured tables. That is exactly where classic OCR collapses. We pair vision models with rules for known formats (commercial-invoice templates) and zero-shot extraction for everything else.
Every IDP pipeline we ship looks like this loop. Documents arrive from email, S3, mobile capture, or your portal. A vision-language model classifies, extracts, and validates inside a confidence-routing layer. Structured data writes back into NetSuite, SAP, Workday, or your own database with idempotency keys and a per-field audit log. No new dashboard for your team to learn.
IDP software like Hyperscience and Rossum, a document ai platform like Google Document AI, and a templated API like Mindee are all real options. So is in-house build. So is custom multi-modal LLM with a harness. Eight dimensions, honestly. Yes, sometimes the audit says 'go buy Mindee.'
Pricing and timelines reflect typical GetWidget engagements; alternative columns are generalisations from public pricing pages, RFP responses, and shipped client work.
A 30-minute fit call — we will tell you honestly whether you need a custom IDP build, a platform vendor, or just better OCR. No pitch.
Four phases, milestone-billed, with explicit kill points. We start with a doc-type and eval-set audit, then design the extraction schema and HITL architecture, then ship one doc-type live end-to-end. If the eval-set accuracy targets will not move on your data, you walk away at the pilot gate. No retainer trap.
Two-week IDP audit. We inventory your doc-types, build a representative eval set (50–200 samples per priority doc-type), map current OCR/manual cost-per-page, and rank workflows by ROI × feasibility.
Model picks per doc-type (Claude Opus 4.7 vision vs GPT-5 vision vs open VLM), extraction schema (Pydantic / Zod / JSON Schema), confidence-routing rules, HITL queue design, and downstream integration contract. You sign off before any pipeline code ships.
One doc-type live end-to-end against real systems. Pilot acceptance = per-field accuracy targets met on the holdout eval set, with confidence routing tuned to a defensible auto-approval rate. Shadow-mode comparison against your current process before any cutover.
Monthly $/page report per doc-type. Model-drift watch on the eval set (we re-score quarterly). Next doc-type onboarded on the same harness. Most clients add doc-type #2 in month two.
Most IDP marketing leads with a headline accuracy number. We refuse to. The only meaningful measurement is per-field accuracy at a defined confidence band on a holdout eval set you sign off on at audit. We score every field × every doc-type × every confidence band, and we report what we found, including the bands where the model is underperforming. The three pillars of the harness below are how we make accuracy claims defensible.
A document with 12 fields can be '92% accurate' overall while having a critical-field accuracy of 70%. We track every field separately and weight by downstream impact. Getting an invoice total wrong matters more than getting the supplier address wrong.
Reporting averages hides the bimodal distribution. We report accuracy at confidence ≥0.9, 0.7–0.9, and <0.7 separately. The auto-approval rate is then a defensible policy choice, not a hand-wave: 'we auto-approve at ≥0.9 because per-field accuracy in that band is 99.2% on your eval set.'
A healthy AP automation or claim-triage pipeline lands roughly 90% auto-processed, 9% routed to a one-click reviewer queue, 1% rejected outright. We tune the hitl confidence routing thresholds to land in that band, not to hit a headline number for the deck.
Most clients begin with the fixed-fee discovery audit to inventory doc-types and design the eval set, then run a 4–8 week pilot on the highest-ROI doc-type, then move to continuous monthly delivery for doc-types two through N on the same harness.
Doc-type inventory, eval-set design, model recommendation, cost-per-page projection.
One doc-type live end-to-end against your real downstream system.
Embedded squad shipping additional doc-types onto the same harness.
The cases below are anonymised capability patterns drawn from real engagements. Numbers are stated as methodology targets and per-engagement bands. We do not publish fake headline percentages. Named references shared under NDA once we know what you are building.
AP team manually keying line items from 2,000+ supplier invoices/month. Multi-page bills and foreign-currency invoices a particular pain. Current OCR vendor stuck at template-level extraction; non-templated invoices kicked to manual.
Multi-modal vision pipeline (GPT-5 for first pass, Claude Sonnet 4.6 for low-confidence retries), Pydantic-validated extraction schema mirroring NetSuite Vendor Bill, 3-way match against PO before posting. Confidence routing: ≥0.85 = auto-post · 0.6–0.85 = AP-clerk one-click review · <0.6 = full manual queue with draft attached.
FNOL document bundles arrive as 10–40 page packets (police reports, photos, medical records, repair estimates). Adjusters spending 15+ min/claim just sorting and routing. Long-tail document types break templated extraction.
Two-stage pipeline: classifier splits bundle into document-type segments, then each segment hits the right extractor (medical-records prompt vs damage-estimate prompt vs police-report prompt). Completeness checker flags missing documents back to the customer before adjuster touches the file.
Corporate KYC onboarding requires 12+ documents per entity (certificate of incorporation, UBO declarations, director IDs, address proofs, bank statements). Compliance team spending entire days assembling and cross-checking bundles before screening.
Per-document extractor with cross-bundle validation: extracted entity names, addresses, and IDs must match across all 12 documents. Mismatches routed to a compliance-analyst queue with diff view. Sanctions/PEP screening NOT replaced. Extracted entities are pushed into the existing screening engine.
Model choice is per doc-type, not per pillar. We default to Claude Opus 4.7 vision for long-context contract work and GPT-5 vision for high-volume invoice extraction, with open VLMs (Qwen2-VL, Llama 4 vision) for PHI-restricted or air-gapped workloads. Mindee, Nanonets, and AWS Textract sit in the stack as platform fallbacks where they win.
We say no a lot. These are the three patterns we see most often where custom IDP is the wrong tool. The audit step will tell you if any apply before you sign a pilot.
If you receive the same insurance form on the same template every day, classical template OCR will be cheaper and more accurate than an LLM. We will tell you to keep your existing OCR vendor on those doc-types. We are not in the business of replacing what works.
If your PDFs have a reliable text layer (most modern bank statements, SaaS-generated invoices, system-of-record exports), a deterministic parser plus a small validator beats vision-LLM cost. We start with `pdfplumber` + Pydantic, not Claude vision.
If a single mis-extracted notarial seal, signature, or financial figure costs you a court case or a regulatory fine, automation is not the play yet. We design HITL-only workflows for those fields and automate everything else around them — honestly.
Book a free IDP audit. We will inventory your doc-types, build a representative eval set, recommend a model per doc-type, and project cost-per-page before any pipeline code ships. No deck, no obligation to build.
Not sure if you need IDP, broader AI integration, or just better OCR? These pages go deeper on the adjacent decisions.
Push extracted data into NetSuite, SAP, Workday: the downstream layer for any IDP pipeline.
Claude Opus 4.7 vision is one of our two primary IDP model picks, with long-context and structured-output strength.
GPT-5 vision is the alternate IDP pick, with mature structured outputs and broad ecosystem support.
Orchestrate the human-in-the-loop queue and downstream side-effects around your IDP extraction.
Full-stack AI engineering for when IDP is one component of a broader product build.
BAA-covered medical record processing with EHR integration on Epic / Cerner / athena.
Where extracted documents become a queryable RAG knowledge base for employees + agents.
Plant-floor IDP for BOMs, work-orders, and spec-sheet extraction on the same OPC UA + ERP spine.
Dated retrieval benchmark on a 1,840-document corpus — the methodology behind how we score IDP retrieval pipelines before scoping a build.