Customer support
Answer detailed product, warranty and policy questions consistently.
Customer Support Automation
RAG & Knowledge-Base Chatbots
RAG Chatbots That Answer From Your Own Documents
A RAG chatbot uses retrieval-augmented generation: before answering, it searches your documents for the most relevant passages and gives them to an AI model, so the answer is based on your information rather than the model’s general knowledge. AVP Tech Solution builds RAG assistants on company documents, product catalogues and SOPs for customer support and internal teams.
- Answers from
- PDFs, Word and Google Docs, web pages, catalogues, SOPs
- Used by
- Customers, sales teams, support teams, new employees
- Shows sources
- Yes — answers can link to the document they came from
- Channels
- Website, WhatsApp, internal tools
What is RAG (retrieval-augmented generation)?
Large language models are good at writing, but they do not know your price list, your refund policy or last month’s product update — and when they do not know, they may guess. RAG fixes this by adding a search step.
Your documents are split into small passages and stored in a searchable index (usually a vector database, which finds passages by meaning rather than exact words). When someone asks a question, the system retrieves the most relevant passages and asks the AI model to answer using only that material, ideally with a link to the source.
Because the knowledge lives in your documents, updating the chatbot is as simple as updating the document — no retraining needed.
How does a RAG chatbot work?
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Ingest
Documents are collected from files, Google Drive, your website or a database.
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Index
Text is split into passages and converted into embeddings stored in a vector database.
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Retrieve
Each question is matched against the index to find the most relevant passages.
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Answer
The AI model writes an answer using only those passages, and cites them.
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Refuse or hand over
If nothing relevant is found, the assistant says so and routes the question to a person.
Who needs a RAG chatbot?
Sales teams
Find specifications, pricing rules and comparison points during a client call.
Sales AutomationStaff training and SOPs
New employees ask the assistant instead of interrupting senior staff.
Document-heavy operations
Search contracts, manuals and internal guides in plain language.
Business Process AutomationWhat makes a RAG chatbot accurate
Most of the quality of a RAG system comes from preparation, not from the AI model. We focus on:
- Clean, current source documents — duplicates and outdated versions removed.
- Sensible splitting, so each passage keeps its context (tables, headings, product names).
- Retrieval tuned and tested against real questions from your team or customers.
- Strict instructions to answer only from retrieved content and to cite sources.
- Access control, so internal-only documents never reach a customer-facing bot.
- A simple way to add or update documents as your business changes.
How we build your knowledge-base assistant
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Audit content
We review which documents exist, which are current, and who should be able to see what.
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Build the pipeline
We set up ingestion, indexing and retrieval, typically orchestrated with n8n.
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Create the assistant
We write the answering rules, citation format and handover behaviour.
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Evaluate
We test with a list of real questions and measure which answers are right, partial or missing.
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Launch and maintain
We deploy to your chosen channel and set up a routine for adding new documents.
Technology
Sources
AI
Search
Orchestration
Channels
How much does a RAG chatbot cost?
The price depends mainly on the volume and messiness of the documents, the number of channels, and access-control needs. We quote a fixed price after reviewing a sample of your content. Running costs include AI model and embedding usage, which is charged per use by the AI provider, and hosting for the vector database and workflows.
FAQ
RAG Knowledge-Base Chatbots: frequently asked questions
RAG stands for retrieval-augmented generation. The system retrieves relevant passages from your own documents and then generates an answer from them with an AI model.
For most business knowledge, yes. RAG is faster and cheaper to build, can show the source of each answer, and is updated by simply changing the documents. Training or fine-tuning a model is better suited to changing style or behaviour than to storing facts that change.
It can, especially if documents are outdated or contradictory, or retrieval finds the wrong passage. We reduce this with clean sources, retrieval testing, citations and a rule to hand over when no good source is found.
We control which documents are indexed, keep internal and public knowledge separate, and send only the retrieved passages needed for each answer to the AI model. Hosting can be arranged on infrastructure you control.
Common formats such as PDF, Word, Google Docs, spreadsheets and web pages. Scanned documents need text extraction first, and complex tables may need extra preparation.
Related services
Works well with
Knowledge-base chatbot
Turn your documents into instant answers
Share a sample of the documents your team searches every day. We will show you how a RAG assistant would answer from them.
Build Your Knowledge-Base ChatbotLast updated 5 October 2026 by the AVP Tech Solution team.