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 4 vision, GPT-4o 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.
Classic intelligent document processing meant OCR plus rules plus per-template training. Modern AI document processing means 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.
AP invoice extraction with line-item, tax-code, and PO-match logic. We push to NetSuite, SAP, QuickBooks, Bill.com with idempotent writes and 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 — 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, Rossum, and Mindee are real options. So is in-house build. So is custom multi-modal LLM with a harness. Eight dimensions, honestly — and 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 4 vision vs GPT-4o 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 routing thresholds to land in that band — not to hit a headline number for the deck.
Most clients begin with the $3K 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 monthly 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-4o for first pass, Claude 4 Sonnet 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 pushed into the existing screening engine.
Model choice is per doc-type, not per pillar. We default to Claude 4 vision for long-context contract work and GPT-4o vision for high-volume invoice extraction, with open VLMs (Qwen2-VL, Llama 3.2 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 — and 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.
Intelligent document processing (IDP) is the discipline of turning unstructured documents — invoices, contracts, claims, medical records, KYC bundles — into structured data your downstream systems can use. In 2026, the meaningful shift is the move away from classic IDP (OCR + rules + per-template training) toward multi-modal vision-language models (Claude 4 vision, GPT-4o vision, open VLMs like Qwen2-VL) that can extract from doc-types they have never seen before, with a Pydantic-validated schema and confidence routing on the back end. The category term has not changed; the engineering inside it has.
OCR turns pixels into text. AI document processing — modern IDP — does four things on top of OCR: (1) classifies the document type, (2) extracts fields into a strict schema, (3) validates the extraction against business rules (does this PO number exist? does this date fall in our fiscal year?), and (4) routes the result based on confidence (auto-approve, send to reviewer, or reject). OCR is a component inside an IDP pipeline; it is not the same thing. The PAA on this SERP keeps surfacing the OCR-vs-IDP question because most buyers learned the category in the OCR era.
It comes down to five questions. (1) How stable are your doc-types? Stable + high-volume = platform. Long-tail = custom. (2) Do you need a bespoke extraction schema mapped 1:1 to your downstream system? Custom wins. (3) How important is downstream integration depth (NetSuite, SAP, Workday)? Custom wins — and we cover the connector layer on <a href="/services/ai-integration-services/">AI integration services</a>. (4) Cost floor — Hyperscience and Rossum carry annual platform licences; a custom pilot starts at $10–25K with no recurring SaaS fee on top. (5) Vendor lock-in tolerance — model-agnostic custom builds let you swap between <a href="/services/claude-development/">Claude</a> and <a href="/services/openai-development/">OpenAI</a> in a single variable. We have shipped both buy-then-extend and full-custom engagements; the audit picks the right one before you sign.
Honest answer: it depends on the doc-type variability, the schema, and the eval definition. We refuse to publish 'we get 99% accuracy on invoices' marketing — the only meaningful number is per-field accuracy at a defined confidence band on a holdout eval set. In a typical AP automation pilot we aim for a 90 / 9 / 1 split — 90% auto-post, 9% one-click reviewer, 1% rejected — measured against an eval set you sign off on at audit. If your invoices are unusually variable, that band shifts; we report it before the pilot starts, not after.
Good at: FNOL intake routing, multi-document bundle classification, completeness checks (is the police report attached? are the medical records up to the date of incident?), and drafting summaries for the adjuster. Bad at: liability decisions, edge medical interpretation, and anything contested — those stay human until your regulator and your legal team say otherwise. We design claim pipelines that compress adjuster time on the assembly work and leave the judgement calls intact.
Zero-shot. We use Claude 4 or GPT-4o vision as the first-pass classifier with a structured prompt that lists your known doc-types plus an 'other' bucket. New doc-types surface in the 'other' bucket, get reviewed by a human once, and the prompt is updated — no labelling platform, no retraining job, no 6-week onboarding. For high-volume doc-types we add a small fine-tune or a deterministic header rule on top, but the default is zero-shot with a human-confirm loop until the doc-type stabilises.
Yes — by building inside your compliant environment. For HIPAA-covered medical record work we use Claude via AWS Bedrock with a BAA, Azure OpenAI with retention disabled, or self-hosted open VLMs (Qwen2-VL, Llama 3.2 Vision) on your VPC. For SOC 2 we deliver against your existing controls and produce the audit logs and retention configuration your auditor needs. For GDPR we keep processing inside your data-residency region (EU/US/India). Honest disclosure: we are NOT a HIPAA-certified IDP platform — we build pipelines inside environments that already are.
Three tiers. $3K audit (1–2 weeks) — doc-type inventory, eval-set design, model recommendation, build-vs-buy call. $10–25K pilot (4–8 weeks) — one doc-type live end-to-end with downstream integration. From $5K/month continuous — embedded squad shipping doc-types two through N on the same harness, with monthly $/page reports. Walk-away point at the end of pilot — if accuracy targets won't move on your eval set, no phase 2.
Three disqualifiers. (1) Stable single-template forms with deterministic field positions — classical template OCR is cheaper. (2) PDFs with reliable text layers (modern bank statements, SaaS-generated invoices) — `pdfplumber` plus a small Pydantic validator beats vision-LLM cost. (3) Legally-binding fields where a single mis-extraction costs you a court case or regulatory fine — design HITL-only on those fields. We will tell you all three at the audit if any apply, and we do not run pilots on workflows that should not exist.
Through a JSON contract from the extractor into a downstream connector with retry, idempotency, and audit log. We have shipped writebacks into NetSuite (SuiteTalk REST · token-based auth · idempotency keys on Vendor Bills), SAP (BAPI / OData), Workday (Web Services), and dozens of custom Postgres / Mongo / Snowflake schemas. The deeper integration design is covered on our sibling pillar — see <a href="/services/ai-integration-services/">AI integration services</a> for the broader system-integration model.
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.
Read more 02Claude 4 vision is one of our two primary IDP model picks — long-context, structured-output strong.
Read more 03GPT-4o vision is the alternate IDP pick — mature structured outputs, broad ecosystem support.
Read more 04Orchestrate the human-in-the-loop queue and downstream side-effects around your IDP extraction.
Read more 05Full-stack AI engineering — when IDP is one component of a broader product build.
Read more 06BAA-covered medical record processing — EHR integration on Epic / Cerner / athena.
Read more