AI Document Intelligence Services

AI document intelligence is the combination of optical character recognition (OCR), large language model (LLM) extraction, and structured data pipelines to automatically read documents and extract information into usable data. Traditional OCR reads text from images and PDFs but produces raw text, not structured fields. AI document intelligence goes further by understanding the meaning and context of extracted text, classifying documents by type, mapping fields to your target data schema, and validating output against business rules before it reaches your system.

We build systems for: invoices and purchase orders (extracting vendor, line items, totals, and tax), contracts and agreements (extracting parties, dates, terms, and key clauses), forms and applications (extracting field values from structured forms regardless of layout variation), shipping and logistics documents (bills of lading, packing lists, delivery notes), identity documents (passports, driving licences, ID cards for KYC), medical and clinical documents (lab reports, prescriptions, referral letters), and industry-specific documents (certificates, inspection reports, warranty claims). The AI approaches each document type differently based on its structure and the extraction requirements.

Traditional OCR reads text and produces a text string. Rule-based extraction then tries to find fields by position or pattern, it breaks when the document layout changes. LLM-based extraction reads the document as a language model would, understanding context, inferences, and relationships between fields. It can extract "the total amount excluding VAT" from a document where it's described in several different ways across different vendor templates. It handles variation that breaks rule-based systems. We use LLMs for complex or variable documents and rule-based extraction for high-volume, consistent document types where speed and cost matter more than flexibility.

For high-quality digital PDFs and consistent document types, accuracy is typically 95–99%. For scanned documents, accuracy depends on scan quality. We improve accuracy through document pre-processing (image enhancement, deskewing), vendor-specific templates for high-volume document sources, confidence scoring that flags low-confidence extractions for human review, and validation rules that cross-check extracted values against expected formats, ranges, and business rules. Most production systems reach 80–95% straight-through processing rates, meaning only 5–20% of documents require any human review.

Every production document intelligence system has an exception path. Low-confidence extractions and documents that fail validation are routed to a human review queue. Reviewers see the original document and the extracted fields side by side, correct any errors, and confirm the extraction. Corrections feed back into the system to improve future accuracy for similar documents. The exception queue is designed to minimise review time, a reviewer typically handles an exception in under 60 seconds.

A focused document extraction system, one document type, validation rules, and output delivery to one target system, typically runs $25,000--$60,000. Multi-document type platforms with complex extraction logic, exception workflows, and multiple output integrations run $60,000--$150,000. We've built production OCR and AI extraction systems including a gas station fuel delivery invoice system. We scope every project before pricing it.