AI Document Analysis That Knows Your Business

RAG stands for Retrieval-Augmented Generation. In plain English: instead of asking a generic AI chatbot that only knows what it was trained on, a RAG system searches your own documents first, finds the relevant passages, and then uses an AI language model to write a clear answer based only on what it found. The result is accurate, cited responses from your private data — not guesses from the internet. Think of it as giving your documents a brain.

Your data stays entirely private. We configure all API calls to opt out of model training, and for Manchester clients with particularly sensitive data (healthcare, legal, financial), we can deploy models that run on your own infrastructure — meaning your documents never leave your servers. We sign data processing agreements as required by UK GDPR, and all systems include audit logs showing exactly who queried what and when.

RAG significantly reduces hallucination compared to standard LLM chatbots because the AI is constrained to answer only from retrieved document passages. Every answer includes citations pointing to the exact source document and paragraph. In our benchmarking, well-built RAG systems achieve 90–95% factual accuracy on domain-specific queries. Where the answer is not in the documents, the system says so — rather than making something up. For legal and medical applications, we build additional verification layers and human-in-the-loop checkpoints.

RAG systems can ingest almost any text-based document: PDFs (including scanned documents with OCR), Word documents, Excel spreadsheets, PowerPoint presentations, web pages, emails, SharePoint libraries, Notion workspaces, Google Drive files, and plain text. For Manchester law firms, this means case files, contracts, precedents, and correspondence. For healthcare, clinical guidelines, patient notes (with appropriate consent), and regulatory documentation. If you have a document format we have not listed, ask — we almost certainly support it.

The Discovery phase takes 2–3 weeks and gives you a proof of concept you can show stakeholders. A Proof of Concept build takes 6–10 weeks from kick-off to handover. A full Production deployment typically runs 12–20 weeks depending on the number of document sources, the complexity of your access control requirements, and the integrations needed. For Manchester law firms and healthcare providers with large document volumes, we always recommend starting with Discovery to get accurate scoping before committing to the full build.

Our Discovery engagement starts at £5,000 and gives you a working prototype plus a clear cost-benefit analysis for a full build. Production RAG systems typically range from £40,000 to £80,000 depending on scope. The ROI case is strongest where staff currently spend significant time searching for information: a 50-person Manchester law firm where fee earners spend 2 hours per week searching documents could recover over £200,000 in billable time annually. We always provide a projected payback period as part of the Discovery deliverables.

ChatGPT knows nothing about your specific documents, your clients, your precedents, or your internal processes. It answers from general internet training data, which means it can give plausible-sounding but factually wrong answers about your business. A RAG system built for your organisation answers only from your documents, cites the exact source, respects your access controls (different staff see different documents), and runs on infrastructure that keeps your data private. For Manchester businesses in regulated industries — legal, healthcare, financial services — this distinction is not just useful, it is essential.