RAG & Knowledge Base Chatbots
- RAG pipelines built on your own documents, data, or knowledge base
- Vector search with proper chunking so answers stay accurate
- Deployed as a chatbot, internal tool, or API — your call
A model on its own doesn't know your product docs, your support history, or your internal wiki. RAG (retrieval-augmented generation) fixes that: your content gets indexed, and the model answers by pulling the actual relevant passage first, rather than guessing from training data.
LangChain for the retrieval pipeline, OpenAI for embeddings and generation, a vector store sized to the data (from a lightweight setup to a dedicated database depending on volume), Next.js/Node.js for the interface or API layer.
Document ingestion and chunking, embedding pipeline, retrieval tuning so answers actually match the source material, and a front end — chatbot widget, internal search tool, or API, depending on what's needed.
Internal wikis employees can actually query instead of searching a shared drive, support bots that answer from real documentation, and product search that understands intent rather than exact keyword matches.
Common questions
What's the difference between this and just using ChatGPT?
ChatGPT answers from general training data. A RAG system answers from your specific documents, so it stays accurate to your product, policies, or internal content — including things that changed after the model's training cutoff.
What kind of data can this work with?
PDFs, internal wikis, support tickets, product documentation, spreadsheets — most text-based content can be indexed and made queryable.
How do you stop the bot from making things up?
Retrieval is tuned so the model only answers from what it actually finds in your data, and can be set to say "I don't know" rather than guessing when nothing relevant turns up.
Does this need to be public-facing, or can it stay internal?
Either — an internal tool behind a login, or a customer-facing chatbot on the site. Scope depends on the use case.
How often does the knowledge base need to be updated?
Depends on how often the source content changes — re-indexing can be a manual step or automated on a schedule.
See also
Best plan offer
Basic
Delivery: 2-3 days
- Full-stack website, up to 5 pages
- React/Next.js + Node.js + SQL/MongoDB
- Database integration + API development
- Fully responsive design
Standard
Delivery: 5-10 days
- Full-stack website, 5-7 pages
- React/Next.js + Node.js + SQL/MongoDB
- Authentication system (JWT/Auth.js)
- AI features integrated (chat, search, or content)
- Custom UI components
Premium
Delivery: 15-30 days
- AI automation / agentic system, built to spec
- Custom agent workflows (LLM-driven, tool-using)
- Integrations with your existing tools/APIs
- Task/process automation to replace manual work
- Admin dashboard + monitoring