RAG Knowledge Assistant

Solution showcase: a reference build that shows how we design and deliver this type of system. Every project is tailored to the client’s tools, data and processes.

A private AI assistant that answers questions from your own documents, including policies, product guides, help articles and contracts. Every answer cites its source, so people can trust it.

The challenge

Knowledge is scattered across PDFs, wikis, drives and help centres. Staff interrupt each other for answers, customers wait for support, and new hires take months to find their way around.

What the solution does

• Answers questions in plain language from approved company content
• Cites the exact document and section behind every answer
• Respects user permissions, so people only see what they are allowed to
• Works on the website, in Slack or Teams, or inside internal tools
• Hands over to a human when it is not confident
• Tracks unanswered questions to show where documentation is missing

How it works

1. Documents are collected, cleaned and split into meaningful sections.
2. Each section is turned into an embedding and stored in a vector database.
3. For each question, the most relevant sections are retrieved.
4. A language model writes the answer using only that retrieved content, with citations.
5. Content syncs automatically when documents change.

Tech stack

• OpenAI / Claude
• LangChain or LlamaIndex
• Vector database (pgvector or Pinecone)
• Python FastAPI
• React chat interface
• Slack / Teams integration

Who it is for

Support teams, HR and operations, training and onboarding, and education providers that need accurate answers from large document sets.

Related services

AI Chatbot Development · LLM Fine-Tuning · AI Agent Development