RAG chatbot development — AI trained on your data
Generic AI chatbots hallucinate facts and give wrong answers. RAG chatbots are different - they're grounded in your actual data. Trained on your docs, website, products, and knowledge base, they deliver accurate answers your customers and team can trust.
Generic chatbots do more damage
than having no chatbot at all
Confidently Wrong
ChatGPT and generic AI chatbots make up answers when they don't know. One confident wrong answer destroys customer trust - and your reputation.
Can't Find Answers in Your Docs
Customers give up when your help docs are buried and hard to search. Support tickets pile up for questions already answered somewhere in your knowledge base.
Support Costs That Don't Scale
Hiring more support staff for the same repetitive questions is an endless cost. A RAG chatbot answers the same question a thousand times without fatigue or salary.
Can't Use Proprietary Information
Your pricing, policies, product specs, and internal processes can't go into a public AI model. RAG keeps your data private while making it accessible.
RAG explained simply
RAG stands for Retrieval Augmented Generation. It means the AI retrieves relevant information from your documents first, then generates an answer grounded in what it found - not what it guessed.
Outcomes, not just deliverables
We don't sell features. We sell results. Here's exactly what you'll walk away with.
Zero Hallucinations
Answers grounded exclusively in your documents - the AI can't make up information it wasn't given.
Multi-Format Knowledge Ingestion
Trained on PDFs, Word docs, website content, Notion pages, Confluence, Google Docs - wherever your knowledge lives.
Source Citations
Every answer includes links to the source document so users can verify and explore further.
Private & Secure
Your data never goes to OpenAI for training. Deployed in your own cloud infrastructure with full data sovereignty.
Human Escalation
When the chatbot can't confidently answer, it escalates to a human - with context so your team picks up seamlessly.
Analytics Dashboard
See the questions your customers ask most, gaps in your knowledge base, and satisfaction scores - to continuously improve.
Common questions
PDFs, Word documents, Excel files, website pages, Notion databases, Confluence wikis, Google Docs, and plain text. We ingest whatever format your knowledge lives in.
For questions your documents answer - very accurate. RAG chatbots don't guess. If the answer isn't in your documents, the bot says so and escalates to a human.
Completely. Your documents are stored and processed in your own cloud environment. They are never sent to OpenAI or Anthropic for model training. You retain full ownership.
A focused chatbot with a defined knowledge base typically takes 4–8 weeks. Complex implementations with multiple data sources and deep integrations take 10–14 weeks.
Yes. The underlying models support 50+ languages. If your knowledge base is in English, the chatbot can answer questions asked in other languages by retrieving English content and translating its response.
Ready to give your customers instant, accurate answers?
Book a discovery call. We'll review your knowledge base, define the scope, and show you exactly what your chatbot would look like.