Document AI
Relytic builds document-intelligence pipelines that extract, understand, structure, and validate that information so it can be used by your teams and systems.
The problem
Document-heavy workflows create friction when people have to repeatedly:
Traditional OCR only solves part of this problem. The useful question is whether the information, structure, relationships, and context needed by the workflow are preserved correctly.
Extract text and structure from digital PDFs, scans, forms, and mixed document collections.
Turn unstructured documents into validated fields, records, JSON, tables, or other machine-readable formats.
Preserve rows, columns, headers, empty cells, and relationships so downstream systems receive the actual table structure rather than flattened text.
Identify document type, topic, language, or workflow category and automatically route files to the appropriate process.
Compare contracts, reports, forms, or specifications and surface differences, missing information, or relevant evidence.
Process information contained in charts, diagrams, images, and page layouts when text extraction alone is not enough.
Build ingestion, processing, validation, storage, monitoring, and retry infrastructure for high-volume document workflows.
System anatomy
Depending on the workflow, a production system may combine:
We select the components based on the document types and the business information that must survive the pipeline.
Task-specific evaluation
An OCR system can score well on general text metrics and still fail the business workflow.
Two systems may extract almost identical text from a table while one places values under the wrong columns.
Evaluation should reflect the actual document structure and downstream use case.
Evidence
Production document work is judged on representative business files and downstream usefulness, not clean demos.
01
Relevant work has included evaluating Azure Document Intelligence, Docling, GLM-OCR, and multimodal document-processing approaches on representative business documents, including complex tables.
Tools and methods
02
The evaluation considered general OCR quality alongside table structure and downstream usefulness. GLM-OCR was selected for a custom pipeline on the document types that mattered most while reducing dependence on a costly external API.
03
The broader knowledge environment processes hundreds of thousands of documents across 7 languages and supports thousands of queries per day.
Extract defined clauses, dates, parties, obligations, structured fields, and supporting evidence from long agreements.
Identify required information, missing evidence, inconsistencies, and cases requiring expert review.
Turn incoming forms and supporting documents into structured records while validating required fields.
Extract data from complex reports containing text, tables, figures, and diagrams.
Capture structured information from repeated document types and integrate it into existing systems.
Convert difficult PDFs, scans, tables, and visual documents into clean structured content suitable for search or RAG systems.
Honest advice
Document AI and RAG often work together, but they solve different problems.
If the goal is to extract, classify, validate, or transform information from documents, the core problem is Document AI. If the goal is to search a knowledge base and answer questions using retrieved evidence, the core problem is RAG.
Some projects need both: document intelligence first turns files into reliable structured content, then the knowledge system makes that content searchable.
How we work
We inspect representative files, formats, languages, layouts, variability, and the information the business actually needs.
We establish the target schema and build representative evaluation cases before choosing the processing approach.
We test OCR, parsing, vision, extraction, and validation approaches against the benchmark rather than selecting tools based on demos alone.
We connect document ingestion and structured outputs to the systems where the data needs to go.
We track processing failures and low-confidence cases and use them to improve the pipeline over time.
We inspect representative files, formats, languages, layouts, variability, and the information the business actually needs.
We establish the target schema and build representative evaluation cases before choosing the processing approach.
We test OCR, parsing, vision, extraction, and validation approaches against the benchmark rather than selecting tools based on demos alone.
We connect document ingestion and structured outputs to the systems where the data needs to go.
We track processing failures and low-confidence cases and use them to improve the pipeline over time.
Next step
Book a 30-minute conversation with Relytic to show us the documents, the information you need from them, and what currently requires manual review.