A Chatbot That Knows Your Business:
A RAG chatbot (retrieval augmented generation) is a chatbot grounded in your own documents and data. Before answering, it retrieves the most relevant passages from your knowledge base, then uses a large language model to compose a response based on what it found. Answers come from your policies, manuals and records rather than the model's general training, so they are accurate, current and specific to your organisation.
We build RAG chatbots end to end: ingestion, retrieval, evaluation and private hosting in the UK, delivered as a managed service.
Key Features:
Grounded in Your Company Data
We ingest the documents your teams actually rely on: policies, product manuals, support tickets, wikis, contracts and intranet content, including sources held in SharePoint or Nextcloud. Content is split into well-formed chunks, converted into embeddings and indexed in a vector database, so every question is matched against the most relevant passages in your knowledge base.
Citations Back to Source
Every answer links back to the documents it was drawn from, down to the page or section. Users can verify claims in one click, and your organisation can trust the chatbot because nothing it says is unsupported.
Accuracy You Can Measure
We build an evaluation set of real questions with agreed correct answers, then test the chatbot against it before and after every change. Retrieval logs record what was searched, what was found and what was answered, giving you a full audit trail.
Guardrails for Out-of-Scope Questions
When the knowledge base does not contain an answer, the chatbot says so rather than guessing. Topic boundaries, content filters and escalation paths keep responses safe, on-brand and within policy.
Private Hosting in the UK
Your chatbot runs on our private GPU infrastructure or your own, built on the same foundations as our PrivateGPT and Hybrid AI Platform services. Prompts, documents and embeddings never leave your infrastructure, which keeps GDPR compliance straightforward and removes third-party AI providers from your risk register.
Integrated With Your Systems
Single sign-on through Keycloak means the chatbot respects each user's permissions: people only get answers from documents they are allowed to see. Embed it in your website, Slack or Microsoft Teams, or your internal tools, and connect it to business systems such as CRMs and ticketing platforms via APIs.
How RAG Chatbot Development Works
Retrieval augmented generation solves the two biggest problems with generic chatbots: they do not know your business, and they sometimes invent answers. A RAG chatbot fixes both by separating knowledge from language. The knowledge lives in your documents, indexed and searchable. The language model's job is only to read the retrieved passages and express them clearly. If the retrieval step finds nothing relevant, the chatbot declines to answer instead of improvising.
The build starts with ingestion. We connect to your document sources, whether that is SharePoint libraries, Nextcloud folders, a ticketing system, a wiki or plain file shares, and we set up pipelines that keep the index fresh as content changes. Documents are cleaned, chunked into passages that preserve their meaning, and converted into embeddings: numerical representations that let the system find conceptually similar content, not just keyword matches. Those embeddings live in a vector database that returns the best candidate passages in milliseconds.
Quality is engineered, not assumed. Together we assemble a test set of genuine questions from your staff or customers, agree what good answers look like, and measure the chatbot against that benchmark. Every tuning decision, from chunk size to retrieval depth to prompt wording, is judged by whether it improves the score. Retrieval logs capture each interaction, so when an answer is questioned you can see exactly which sources were used and why.
Privacy is built in from the first day. The entire stack, including the language model, the vector database and the document store, runs on private GPU infrastructure in the UK. There are no calls to external AI APIs, no data sharing with model vendors, and no ambiguity about where your information sits. For regulated organisations in healthcare, finance, legal and the public sector, that single design decision removes most of the compliance burden before it starts.
Our engagement model is deliberately low risk. We begin with a proof of concept on a bounded document set, typically delivered in weeks rather than months. You put real questions to it, we evaluate accuracy together against the test set, and you decide whether to proceed with evidence in hand. Production hardening then adds monitoring, access control, content refresh pipelines and capacity planning, and we operate the whole platform for you as a managed service.
Common Use Cases
Customer Support Deflection: answer routine product and account questions instantly from your help articles and manuals, reducing ticket volume and freeing agents for complex cases.
Internal Knowledge Assistant: give staff one place to ask about processes, tooling and company knowledge that is currently scattered across wikis, drives and inboxes.
Policy and Compliance Q&A: let employees query HR policies, security standards and regulatory guidance, with every answer cited back to the controlling document.
Onboarding: help new starters find answers themselves in their first weeks, drawing on handbooks, training material and team documentation.
Why a Grounded Chatbot Beats a Generic One:
A generic chatbot knows the internet up to its training date. It does not know your prices, your policies, your products or your customers, and when it is unsure it can guess convincingly. A RAG chatbot built on your own knowledge base answers from documents you control, cites its sources, stays current as your content changes, and admits when it does not know. That is the difference between a novelty and a tool your organisation can rely on, and it is what we build.
Request more details about RAG chatbot development.
Drop us a line, and our team will be in touch shortly to discuss your knowledge base, your use case and a proof of concept.