Standard AI generates answers from its training data — billions of web pages, books, and articles that have nothing to do with your business, your policies, or your products. The result is hallucinations, off-topic responses, and confident-sounding answers that are simply wrong.
RAG — Retrieval-Augmented Generation — works differently. Before generating any answer, the AI searches your own uploaded documents using vector similarity retrieval. It finds the most relevant passages from your verified knowledge base, then generates an answer grounded exclusively in that content. It cites the source. It stays on topic. It doesn't invent.
| Capability | RAGbot AI ✓ | Standard AI ✗ |
|---|---|---|
| Source of answers | Your documents | Internet training data |
| Hallucination risk | None | Moderate–High |
| Policy-aligned answers | Always | Not guaranteed |
| Source citation | Every answer | No |
| Instant doc updates | Yes — zero delay | Requires retraining |
| Knows its limits | Escalates gracefully | May guess anyway |
| UK hosted | Always | Often US/EU only |
Upload. Index. Deploy. Your team goes from answering everything manually to handling only what truly needs human judgment.
Upload PDFs, Word documents, plain text, and web page URLs. Product manuals, HR handbooks, return policies, technical FAQs, compliance guides, pricing documents, SOPs — if it's written down, the AI can learn from it. No limits on document count or size.
Change a pricing page, upload a revised policy, or paste in a new FAQ — the AI reflects the update immediately. No retraining pipeline. No cache flush. No scheduled deployment window. Publish a document at 2 PM, the AI answers from it at 2:01 PM.
Configure separate knowledge bases for Support, Sales, HR, Finance, and Operations. Your support AI draws from troubleshooting guides; your sales AI accesses product comparison sheets. Each team's AI stays precisely on-topic — no knowledge bleed between departments.
In hybrid mode, operators handle conversations while RAGbot works in the background. As each visitor message arrives, the AI instantly retrieves relevant knowledge passages and suggests a reply. The operator accepts, edits, or rewrites — dramatically accelerating time-to-response with zero knowledge gap.
Every conversation is summarised automatically by the AI — capturing visitor intent, key data points, outcome, and recommended next action. Operators get instant briefings at handoff. Managers get searchable, structured records without reading entire transcript logs.
RAGbot analyses conversation patterns over time, identifies recurring questions not well-covered in existing documents, and flags knowledge gaps for your team to address. The quality of answers improves continuously as your knowledge base matures and as real conversation data informs what visitors actually need to know.
When human expertise is needed, the AI detects the escalation signal and transfers the conversation with zero friction. The operator receives the full transcript, visitor intent summary, and handoff reason before they type a single character. No repetition. No context loss. Immediate effective engagement.
Combine RAG AI with the visual workflow builder for complete control. Build pre-chat intake flows that collect visitor context, then hand off to the RAG AI agent with all captured data included. The AI has everything it needs before the visitor asks their first free-text question.
All documents, conversation data, and AI processing happen on UK-hosted infrastructure only. Full audit log of every AI answer — including the source document cited. Designed for teams in regulated sectors: legal, finance, healthcare, public sector, and HR where compliance and accountability are non-negotiable.
RAGbot doesn't replace your team. It removes every bottleneck between what they know and how fast they can use it — turning every operator into your best-informed expert, from day one.
An operator typically remembers the top 20 FAQs. RAGbot retrieves from 2,000 pages of documentation in under a second. Your operators answer every question as if they've worked there for a decade, from their first hour on the job.
New starters don't accidentally give out-of-date pricing. Senior staff don't paraphrase policy incorrectly under pressure. RAGbot suggestions are always pulled from the current approved version of every document — guaranteeing consistency that no training programme can match.
87% of incoming queries are answered entirely by the RAG AI — returns questions, order status, opening hours, product specs, policy clarifications — cleared without operator involvement. Your human team focuses exclusively on complex, high-value, emotionally sensitive conversations where judgment genuinely matters.
When a conversation moves from AI to human, the entire conversation history, visitor intent analysis, collected data fields, and handoff reason are pre-loaded for the incoming operator. They read a briefing, not a chat log. They act, they don't scramble. Every handoff is a warm one — not a cold transfer to someone starting from scratch.
Log in to the dashboard and upload your documents — PDFs, Word files, URLs, or pasted text. Your return policy. Your product guide. Your HR handbook. Your FAQ article list. Your SLA documentation. Upload everything. The more complete your knowledge base, the sharper the AI's answers. Indexing completes in seconds. Documents are searchable immediately after upload.
Choose your mode — fully autonomous AI, AI with operator oversight, or AI-assisted human-first mode. Assign knowledge bases to departments. Set escalation triggers — the conditions that cause the AI to offer human handoff. Connect your workflow builder to pre-qualify visitors before they reach the AI. All configured through a visual dashboard, no code required.
Paste one JavaScript snippet into your website footer — takes 60 seconds. Your RAGbot-powered chat widget goes live on every page instantly. The AI begins answering immediately from your documents. Operators who need zero AI coverage see 87% of tickets auto-resolved. Those who use smart suggestions cut their response time by multiples. Your knowledge base is live, always-on, and infinitely patient.
RAGbot works for any organisation with structured knowledge that visitors, customers, employees, or patients need to access quickly and accurately.
A general-purpose AI is impressive until you ask it something specific about your business. "What's our returns window?" It might say 30 days. It might say 14. It might describe a returns process from a competitor who trained on better-indexed content. This isn't a failure of intelligence — it's a fundamental limitation of training data. The model genuinely doesn't know what your policy says today, revised last Tuesday.
Retrieval-Augmented Generation solves this at the architectural level. Before the model generates a single token, it searches your document index for relevant passages using vector embeddings — a mathematical representation of semantic meaning that finds content related to the question, not just containing the same words. The top-matching chunks from your verified documentation are injected into the model's context window. The generated response is synthesised from that retrieved content, not from general training. The result is an AI that speaks with the authority of your documentation, cites its source, and cannot hallucinate facts that weren't in the documents you provided.
In a regulated sector — financial services, legal, healthcare, public services — every customer-facing answer carries risk. An incorrect response about a financial product constitutes mis-selling. An incorrect response about a medical procedure could cause harm. An incorrect policy description may expose a firm to a complaint or regulatory sanction. This is why many regulated teams have been reluctant to adopt AI in customer contact at all — the liability of hallucination is too high.
RAG AI changes the risk calculus entirely. When the AI can only answer from your approved, reviewed, and signed-off documents, the compliance question becomes: "Is our documentation accurate?" — which is a question teams are already required to answer. The AI response quality ceiling is defined by the quality of your documentation. Control what goes into the knowledge base, and you control what comes out. For FCA-regulated firms, NHS services, council contact teams, and legal practices, this is the first AI configuration where responsible deployment is genuinely viable. Every answer has a traceable source. Every citation points to an approved text. Every escalation is logged.
The most common mistake when deploying a RAG AI is trying to upload everything at once. A 400-page company handbook, the entire help centre, three years of archived policies — indexed together and queried by the AI before the retrieval pipeline is tuned — produces broadly correct but imprecisely targeted responses. The most effective deployments start specific and expand.
Begin with the top 20 questions your support team actually receives — the questions that feel repetitive, that operators answer from memory, that occasionally get answered inconsistently. Write clean, direct answers to each in a single document. Upload it. Test every query variant against it. Measure confidence scores. Then add the next layer — your returns policy, your product specs one-pager, your pricing table. Each addition extends the AI's effective reach without degrading retrieval precision. Within two to four weeks of iterative uploading and testing, most teams reach a point where the AI resolves over 80% of incoming contacts with high confidence — and the knowledge gaps it exposes reveal exactly which documentation your team needs to create next.
Pure AI replacement creates its own class of problems. Visitors with complex, emotionally charged, or genuinely novel situations encounter an AI that is confidently wrong, or that simply loops because it lacks escalation judgment. Trust erodes fast when an AI can't get out of its own way. The volume wins are cancelled by the reputation damage of edge cases handled badly.
Pure human teams face the opposite problem: scale. The 3 AM enquiry goes unanswered and the sale goes to a competitor who has a chatbot (even a poor one). The peak Monday morning hour buries the team in questions that could have been answered from the FAQ. Average handle time rises because operators spend half their working day finding policy information that should be a two-second lookup. Burnout follows. Quality degrades. The hybrid model resolves the tension. AI manages the volume — the predictable, structured, knowledge-answerable majority. Humans manage the exception — the complex, the sensitive, the high-value. Each does what they are genuinely better at. Operators with AI support suggestions are faster and more consistent than operators without. AI with human oversight improves as gap-detection reveals what needs documenting. The combined system is stronger than either alone.
RAGbot doesn't change what your operators do. It removes the friction between them and the right answer — making every interaction faster, more accurate, and less draining on the people doing the work.
Create your free account. Upload your first document within 5 minutes. Watch the AI start answering like a veteran — immediately.
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RAG — Retrieval-Augmented Generation — works by searching your specific uploaded documents before generating any response. The AI answers from retrieved passages of your verified content, not from general internet training. Because the answer is grounded in a specific document passage rather than generated from broad statistical patterns, the model cannot fabricate facts that weren't in your knowledge base. Every answer traces back to a real source in your documentation.
PDFs, Word documents (.docx), plain text files, and web page URLs. This covers product manuals, return policies, HR handbooks, technical FAQs, compliance documentation, pricing guides, SLAs, employee policies, and any other structured written knowledge. There is no hard limit on document count or total size — upload your entire knowledge library.
Immediately. Upload a revised policy, update a product spec, or add a new FAQ and the AI's retrieval index is updated in real time. There is no retraining pipeline, no cache to clear, and no deployment window. If you publish a change at 2 PM, the AI will answer from it at 2:01 PM. This makes RAGbot ideal for businesses where documentation changes frequently or where accuracy at the moment of answer is critical.
Yes. Department-specific knowledge configurations allow Support, Sales, HR, Finance, and Operations to each have their own AI drawing from their own relevant documents. Your support AI cites troubleshooting guides. Your sales AI cites product comparison sheets. There is no cross-contamination of knowledge between department configurations, keeping each AI precisely focused on the queries it will actually receive.
In hybrid mode, human operators handle conversations while the RAG AI monitors in the background. As each visitor message arrives, the AI retrieves the most relevant knowledge passages and pre-drafts a suggested response. The operator sees this suggestion highlighted next to the input field. They can accept it with one click, edit it, or ignore it and write their own. Most operators find their handle time drops significantly — and newer staff find suggestions remove the anxiety of not knowing every policy by heart.
Yes. All processing — document indexing, vector retrieval, conversation handling, and operator data — runs on UK-hosted infrastructure only. No customer or visitor data is processed outside the United Kingdom. Full audit logs of every AI answer and source citation are retained and accessible. For regulated sectors including financial services, legal, and healthcare, this provides the traceability framework required for responsible AI deployment in customer-facing roles.
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