Document Intelligence

AI that reads unstructured documents, extracts structured data, and validates completeness. Not document storage or management - the step where documents become usable data.

Hospitality - Supplier Invoice Processing

A restaurant group received invoices from multiple suppliers, each with different layouts, field positions, and data formats. Manual data entry was slow and error-prone.

Generic document extraction couldn't handle the variation. Custom models were trained per supplier format using labelled datasets across multiple training batches. The pipeline compares outputs from structured field extraction and layout-based table extraction to determine which approach produces better results for each document class. Extracted data is validated against expected schemas and routed into the operational workflow.

Automotive - Deal Pack Validation

Vehicle deal packs contain dozens of documents in inconsistent PDF formats - finance agreements, insurance certificates, registration forms, compliance documents. Staff manually reviewed each pack to check whether everything was present and correctly completed.

A document extraction pipeline reads deal pack PDFs, identifying document types and pulling key fields. The extraction feeds into a validation step that checks completeness against the expected document set for each deal type.

Property - Lease Document Analysis

A property management firm needed metadata extracted from lease documents stored across a document management system. The documents varied in format, age, and structure.

An AI extraction pipeline reads lease documents, pulls structured metadata (dates, parties, terms, clauses), and writes it back to the document system. The extraction handles variation across document formats without requiring manual template configuration per document type.

The Pattern

The same problem appears in every industry that handles documents:

1. Documents arrive in inconsistent formats
2. Someone manually reads them and enters data into a system
3. Errors and missed items create downstream problems

The AI layer sits between document receipt and data entry. It reads the documents, extracts structured data, validates against expected schemas, and flags exceptions for human review. The human effort shifts from reading every document to reviewing the exceptions.

The accuracy requirement varies by context - invoice processing tolerates some error with human review, while compliance document validation requires higher confidence before passing data downstream.

Stack

Azure Form Recognizer (custom and prebuilt models), AWS Textract, Python, workflow automation platforms.