RAG · OCR · Document intelligence
RAG and OCR solutions for trusted document intelligence.
Extract structured data from PDFs, scans, and images, then retrieve grounded answers from approved business knowledge with source citations. Add AI agents only where a workflow needs controlled action and human review.
Validated extraction
Cited answers
Private deployment
RAG & OCR services
RAG and OCR at the core. AI agents for the work that follows.
OCR extracts and structures information from documents. RAG retrieves grounded answers from approved knowledge. AI agents use those results only when a workflow needs controlled action.
RAG systems
Retrieval-augmented generation (RAG)
Search approved company documents and data sources, returning grounded answers with clear source references while preserving access boundaries.
Explore RAG development OCR & document AIOCR and intelligent document processing
Extract text, tables, and key fields from scanned documents, PDFs, forms, invoices, and images, then validate and structure the results.
Explore OCR processing Controlled automationRAG- and OCR-powered AI agents
Use knowledge retrieved by RAG and structured data produced by OCR to prepare outputs, route exceptions, and complete approved workflow steps.
Explore document AI agentsIndustries
Apply RAG and OCR to document-heavy business workflows.
Apply document intelligence, grounded knowledge retrieval, and controlled automation to the work your industry depends on. Explore our industry AI solutions.
Security & governance
Keep documents, knowledge sources, and actions under your control.
Production document intelligence requires more than model performance. We design RAG and OCR systems, including controlled agent workflows, with data boundaries, permissions, review paths, audit trails, and transparent monitoring.
Role-based access
Control who can search sources, review extracted data, approve actions, and manage each AI system.
Human approvals
Send uncertain retrieval results, extracted fields, and sensitive actions to the right reviewer.
Source traceability
Trace source documents, retrieved passages, tool usage, approvals, and workflow outcomes.
Data boundaries
Restrict access to approved repositories, departments, models, tools, and document types.
How it works
Start with one high-value document workflow and scale from there.
Every project follows the same path: a short call, a review of your documents, a scoped pilot with measurable acceptance criteria, then production. AI agents are added only when the workflow needs to act.
6 stepsfrom first call to production
Scoped around your documents, not a package
See how it works- Introductory call to define the document problem
- Review of representative documents and sources
- Written scope with acceptance criteria
- Pilot measured on real questions and fields
- Production deployment, then managed optimisation
Only when neededand always with approval
RAG answers. OCR extracts. Agents take approved actions.
Check the decision guideAdd an agent only when:
- A repeatable step must follow the answer or extraction
- The step updates another system or routes work to someone
- Approval points and audit logging are defined first
- Consequential actions still wait for a named approver
Insights
Worked examples from the engineering side of RAG and OCR.
Articles written from the work, each grounded in public sources or a worked example, and none of them a sales pitch.
Invoice OCR is the easy part. Validation is what lets finance trust the result.
One scanned invoice, nine checks, one low-confidence field, and what the reviewer sees. Then the checks every invoice pipeline should run and how to evaluate it.
Read the invoice OCR articleWhy legal RAG needs hybrid search: reciprocal rank fusion, explained with lawyers' queries
Lawyers ask two kinds of question that no single retrieval method answers well. How hybrid retrieval and reciprocal rank fusion handle both, and what fusion still cannot do.
Read the legal RAG articleAll articles are listed on the Insights page, with an RSS feed.
Contact us
Discuss your RAG or OCR project.
Tell us about your documents, knowledge sources, workflow, data constraints, and deployment preference. We will respond with a practical path from idea to production.