RAG AI Solutions
We turn the documents your business already holds into a knowledge base your team can question in ordinary language. Staff get accurate answers in seconds, drawn from your own manuals and policies with a link to the source.
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Overview
RAG stands for retrieval augmented generation, which describes a straightforward idea. The system searches your documents first and builds its answer from what it finds there, which means the information comes from your business rather than from general knowledge.
It suits organisations with substantial written knowledge and a growing need to reach it quickly. Support teams working from a long product manual. New starters coming up to speed. Technical teams whose specifications live across a wiki, a shared drive and various inboxes. We build these for businesses across the UK, and where the material is confidential we deploy the whole system within your own infrastructure.
The quality of the answers reflects the quality of the source material, so we begin with an audit of what you hold and how current it is. That review is valuable in its own right, because it shows you which areas of your documentation your people rely on most and where a little investment would help them most.
Where the documents and the model both need to remain inside your own network, this works alongside our private AI and data sovereignty service.
What’s included
Answers From Your Own Documents
Staff ask a question in ordinary language and receive an answer drawn from your own manuals, policies and files, so the knowledge in your business becomes genuinely accessible.
Source Citations on Every Answer
Every answer identifies the document it came from and links to it, so your team can verify the detail and read the full context whenever they need to.
Works With What You Already Have
PDFs, Word documents, spreadsheets, wikis, help centres, intranet pages and scanned files. Most of the material your business already holds can be brought in.
Stays Current Automatically
Update a source document and the answers update alongside it, so your knowledge base stays accurate as your business changes.
Retrieval With Follow-Through
The system can act on what it retrieves, so it will find the relevant policy and raise the ticket, or check the procedure and update the record.
How we deliver
Our RAG AI Solutions Process
Step 1: Knowledge Audit
We review what documentation exists, how current it is and what your people most often need to find, which shapes everything that follows.
Step 2: Ingest and Index
Your documents are processed and indexed so the system can search across all of them, including material held in different formats and locations.
Step 3: Test Against Real Questions
We test the system against a list of real questions from your team and tune it until the answers are consistently accurate and properly sourced.
Step 4: Deploy to Your Team
The knowledge base goes live for your users, integrated wherever they will actually use it, whether that is your intranet, your help desk or your website.
Step 5: Review and Improve
We review what people ask and how well it answers, which guides both the tuning and any additions worth making to your documentation.
On this page
Client work
What this looked like for JS Global
We built a RAG system for JS Global that brings their internal data into a single searchable store. Their team queries it in plain language for day to day questions, and it also supports the management side, where the useful thing is seeing what the data says across the whole set rather than tracking down one document. Getting the material into one queryable place is usually the part that takes the effort, and it is what makes everything after it straightforward.
Pricing from
Questions
RAG AI Solutions FAQs
What does a RAG knowledge base cost?
A knowledge base starts at £2,000, which represents around three days of work at £670 per day covering ingestion, indexing and accuracy testing against your real questions. Larger document collections are quoted after the audit, so you have an accurate figure before committing.
How does this differ from a standard chatbot?
A standard chatbot answers from its general training. A RAG system searches your own documents first and cites the source of every answer, which gives your team both the information and the means to verify it.
How do you make sure the answers are reliable?
We audit your documentation first and show you what it covers well and where it would benefit from attention. Many clients find that review useful on its own, and it means the system launches with material that genuinely serves your people.
Which file formats can it read?
PDFs, Word documents, spreadsheets, web pages, wikis, help articles and most text-based formats. Scanned documents are handled with an OCR pass, which we can arrange as part of the build.
Where are our documents stored?
Wherever suits your requirements. Cloud hosting is quicker to establish and more economical. Where your documents are confidential or regulated, we deploy the whole system within your own infrastructure so everything remains inside your network.
Claude Academy training supporting our AI services.
We have completed all 19 certifications currently available, covering practical AI use, automation, development, data integrations and training. This helps us apply Claude more effectively across client projects.
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