We turn your documents, SOPs, Notion and Drive into a RAG system your team can ask in plain English with source citations on every answer. Built on Pinecone, Supabase, and OpenAI. You own the index and the data.
This demo is grounded in Kaizora's own website content, services, FAQs, about, blog posts. Every answer cites the page it came from. Ask off-topic questions and watch it gracefully refuse.
For your business, we ground it in YOUR documents. Demo here uses Kaizora's content as the knowledge base.
A RAG (Retrieval-Augmented Generation) system is an AI that answers questions using your company's actual documents, not its generic training data. It indexes your files, searches them when someone asks a question, and uses an LLM like GPT-4 to write an answer grounded in what was retrieved with citations back to the source document.
Yes, when you have a body of knowledge (SOPs, product docs, contracts, past client deliverables, training material) that staff or customers repeatedly search through. RAG pays for itself the moment it replaces three or more daily "where do I find...?" interruptions. For businesses with five files and one FAQ, plain GPT is enough.
ChatGPT file upload is a chat-session feature with strict limits and no persistence. A Kaizora RAG is a permanent, queryable knowledge base typically 1,000+ documents indexed in Pinecone or Supabase pgvector, integrated with Notion or Drive so it stays fresh, and accessible through Slack, WhatsApp, a web widget or your existing app.
Production-ready RAG builds vary by document count, integration complexity, and accuracy bar. Salt Technologies publishes a "from $15,000" benchmark for enterprise RAG knowledge bases with 85 to 95% accuracy. Kaizora's SMB-scoped builds start lower typically $5,000 to $15,000 for a single-source SMB knowledge base and are scoped on the discovery call.
Well-built RAG systems typically reach 85 to 95% answer accuracy on in-corpus questions, with every answer citing the exact source paragraph so the user can verify that range is the benchmark Salt Technologies publishes for production deployments. Accuracy depends almost entirely on document quality: clean, structured docs in, accurate answers out.
Yes Notion, Google Drive, Dropbox, PDFs, Word docs, Markdown, HTML, and live web pages are all supported. Updates can be pulled on a schedule or triggered on document change.
