AI Document Processing
PDFs in. Clean rows out.
OCR + LLM extraction for invoices, contracts, applications, CVs, and forms. We turn the document mountain into structured data your systems can actually use — with confidence scores you can audit.
Confidence scored
Multilingual
Sub-second per page
invoice-WP-2026-04812.pdf
Westwood Print Ltd.
Park Industrial Estate
Dublin 12, Ireland
INVOICE
WP-2026-04812
14 May 2026
ItemQtyUnitTotal
A4 brochure print run500€2.40€1,200.00
Business cards (10pk)20€14.50€290.00
Banner stands · large8€168.75€1,350.00
Subtotal€2,840.00
VAT 23%€653.20
Total€3,493.20
Extracting · 0%
Structured output
0/9 fields
Schema: invoice.v2→ Accounting system
What we extract
The document mountain, sorted.
Invoices & receipts
Supplier, line items, VAT, totals, PO reference, IBAN — all extracted with confidence scores. Auto-matched to POs and pushed to Xero/QuickBooks/Sage.
Contracts & agreements
Parties, dates, term length, renewal clauses, payment terms, governing law. Flag unusual clauses for legal review.
Forms & applications
Patient intake, loan applications, insurance claims, onboarding forms — handwritten or typed, validated against your schema.
CVs & resumes
Skills, experience, education, location — parsed into your ATS schema, with relevance scoring against open roles.
Inside the extraction
OCR doesn't read meaning. LLMs do.
Traditional OCR gives you raw text. The hard part is knowing which line is the total, which date is the due date, and what to do when the supplier formats things differently every month. That's where the LLM layer earns its cost.
01 · Ingest
Ingest
Receive document via email forward, Drive watcher, upload form, or scanner. PDF / JPG / scanned image — handled uniformly.
02 · OCR pass
OCR pass
Pixel → text. We use AWS Textract / Azure Form Recognizer / Tesseract depending on doc shape. Handles handwriting too.
03 · LLM understanding
LLM understanding
Text → fields. Claude or GPT-4o reads the document context and maps content to your schema, regardless of layout variation.
04 · Confidence scoring
Confidence scoring
Each field gets a 0-100 confidence. Below threshold → human review queue. Above → straight to destination system.
05 · Destination push
Destination push
Clean structured data lands in your CRM, accounting system, ATS, or warehouse via API/webhook/file drop.
Built on
The extraction stack.
AWS TextractAzure Form RecognizerGoogle Document AITesseractClaude (Anthropic)OpenAI GPT-4o VisionLangChainXero / QuickBooks / SageHubSpot / SalesforceS3 / Drive
Honest limits
Three documents AI shouldn't extract alone.
Anything legally binding without sign-off
Contract extraction is great for triage and indexing — but final values used in payments or contracts always need a human checkpoint. We design that gate by default.
Heavily handwritten + niche formats
Old medical records, decades-old handwritten forms, niche regional formats — accuracy drops. We test against your real samples before quoting; no surprises in production.
Documents needing domain expertise
Insurance claims with policy-specific clauses, medical reports with diagnostic codes — extraction is straightforward, but interpretation needs your domain expert. We hand off, we don't replace.
Frequently asked
Document AI questions.
On typed documents (typical printed invoices, contracts, forms) we see 96-99% field-level accuracy. On scanned docs with mild noise, 92-97%. Handwritten forms drop to 85-92% depending on legibility. We benchmark on your actual documents before quoting — no generic promises.
Send us 20 of your worst PDFs.
We'll run them through a no-commitment pilot extraction, report the accuracy honestly, and tell you whether automation makes sense for your real document mix.