Custom RAG & OCR

Private RAG & OCR Document Search — With Source Citations

Private retrieval-augmented generation for legal precedents, client files and technical manuals. Search scanned and digital archives in plain English, review the supporting source passages and avoid uploading documents to a hosted model.

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Private RAG and OCR system turning scanned documents into searchable answers with citations

How It Works

Three Steps to a Searchable Archive

01

Ingest & OCR

Qwen2.5-VL OCR turns scans, photos and PDFs into clean searchable text — even handwriting.

02

Index

Documents are embedded into a self-hosted vector store with hybrid semantic + keyword search.

03

Ask & Cite

A chat interface answers in plain English and displays the source passages and pages used to form its response.

Built For

Where It Pays Off Fastest

Law firms with large precedent libraries
Accountancy practices with complex client archives
Manufacturers with technical documentation
Bilingual & multilingual organisations

Designed for Review

Answers Your Team Can Check

A useful document system must make evidence, access and deployment choices visible — not hide them behind a chat box.

Visible evidence

Answers can show the source document, page and supporting passage for review.

Permission-aware access

Collections and user roles are configured so staff search only the material they are authorised to use.

Private deployment options

Storage, OCR, retrieval and inference can be deployed in an on-premise or agreed UK-region environment.

RAG & OCR FAQ

Questions About Your Archive

What document formats can be indexed?
Typical projects include digital and scanned PDFs, Word files, email exports, images and structured text. We confirm exact formats, scan quality and archive volume during discovery.
How accurate is OCR on scans and handwriting?
Accuracy depends on image quality, language, layout and handwriting. We test representative samples first and can add review steps where extraction quality matters.
Does RAG train a model on our documents?
Not by default. Retrieval-augmented generation searches an index of your approved content and supplies relevant passages to the model at query time. The implementation and any external processing are documented in the agreed design.

Make a Representative Sample Searchable

Show us the document types, languages and questions that matter. We will recommend a practical ingestion and retrieval approach.

Plan a Document Search Project