RAG chatbots that answer from your own content, with sources
A general chatbot guesses. A retrieval-augmented chatbot looks up the answer in your documents, pages and product data first, then replies with the source attached. We build them end to end, from ingestion to evaluation.
RAG Chatbots
Off-the-shelf chat widgets either know nothing about your business or answer confidently from general training data, which is how they end up inventing policies and prices. Retrieval-augmented generation, or RAG, fixes this by searching your own content for the relevant passages at the moment a question is asked and instructing the model to answer only from what it found.
The quality of a RAG system depends on the unglamorous parts: how documents are cleaned and split into chunks, which embedding model and vector database are used, whether retrieval combines semantic and keyword search, and how answers are tested. We handle all of it, and we build evaluation sets from your real questions so accuracy is measured rather than assumed.
In one recent build, a GPT-5 chatbot that answers from a client's site pages and documents, with sources shown on every reply, reduced the repeat support emails their team had been handling. That is the goal: fewer routine questions reaching your people, and clear, checkable answers for the customers who ask them.
What You Get
Content ingestion
We connect your website, PDFs, help desk articles, shared drives or database records, clean the text, and keep the index updated when content changes.
Chunking and embeddings
We split content into sensible sections with titles and metadata, choose an embedding model suited to your language and subject, and store vectors in Pinecone, pgvector, Qdrant or Chroma.
Hybrid retrieval and reranking
We combine semantic search with keyword matching and filters such as product, region or document type, then rerank results so the model sees the most relevant passages first.
Grounded answers with sources
The model is instructed to answer only from retrieved content, link the pages it used and say when it does not know, instead of improvising an answer.
Guardrails and handoff
We add topic limits, prompt-injection defenses, personal data handling and a clear route to a human by email, ticket or live chat when the bot cannot help.
Evaluation and monitoring
We test against a set of real questions with expected answers, score accuracy and source quality, and log conversations so gaps in your content become visible.
Our Process
Scope and sources
We agree what the bot should and should not answer, who will use it, and which content sources it is allowed to draw on.
Build the pipeline
We set up ingestion, chunking, embeddings and the vector database, then connect retrieval to the language model through a FastAPI backend or your existing stack.
Test with your questions
We run the evaluation set and tune chunking, retrieval and prompts until answers are accurate and sourced, then share the results with you.
Launch and tune
We deploy the chat interface on your site or internal tools, review real conversations and refine content and settings based on what people actually ask.
Deliverables
- A working RAG chatbot embedded on your website, help center or internal tool
- An ingestion pipeline that keeps the index in sync with your content
- A vector database configured for your data, hosted on your own account where possible
- An evaluation set and accuracy report based on your real questions
- A conversation log showing unanswered questions and handoffs
- Documentation covering architecture, running costs, model settings and how to update content
Tools & Platforms
We pick tools for your project, not the other way round. If you already use a platform, we work inside it.
Related Results
RAG chatbot and AI knowledge base
Visitors emailed the same questions again and again. A GPT-5 chatbot trained on the site’s own pages and documents now answers them with sources, around the clock.
AI shopping assistant and semantic product search
Keyword search missed products when shoppers described what they wanted in their own words. Semantic search with exact-match filters fixed that, answering in under 200 ms.
Growing an education publisher past a million pageviews
Speed and SEO work took a speech-language education publisher past one million pageviews and doubled traffic on a niche sister site. Two ecommerce sites were then merged into one domain.
RAG Chatbots: Questions Answered
Is our data used to train the AI model?
On the paid business APIs from OpenAI, Anthropic and Google, data you send is not used to train their models by default under their current terms, and we confirm the terms for the provider you choose. Your documents sit in a vector database we set up on your account where possible, API keys stay on the server, and personal data can be excluded or masked before indexing.
What does a RAG chatbot cost to build and run?
Build cost depends on the number and messiness of your sources, integrations such as a help desk or CRM, languages, user permissions and how much evaluation is needed. Running cost is mainly model usage and vector database hosting, which scale with traffic. We agree a fixed build price first and estimate your monthly running costs before launch.
How long does it take?
A focused bot over a clean website and a set of documents can usually be built in a few weeks. Projects with many sources, permission levels or deep integrations take longer. We give you a schedule along with the fixed scope before any work starts.
Which platforms can it run on?
WordPress, WooCommerce, Shopify, custom web apps and internal tools. The backend runs as a small API service, so the same assistant can serve your website, a help center and a staff tool at once. It can also run privately behind your login for internal documents.
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Explore API & Third-Party IntegrationsLet's Talk About RAG Chatbots
Send a short brief and get a fixed scope, timeline and price, plus an honest view of what is worth doing first.
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