Production-ready RAG · in minutes
Ship answers, not infra.
Rafay turns docs, repos, and connected sources into a grounded, testable chatbot — so builders can ship AI features instead of wrestling chunking, embeddings, and vector ops.
- Minutes
- to first chat
- Citations
- on every answer
- Zero-config
- defaults first
project · knowledge-bot
Sources
3 ready- Product FAQ.mdIndexed
- Support runbook.txtIndexed
- Pricing notesIndexed
Builder workspace · sources → index → grounded chat
Built for developers & technical founders
Internal tools · prototypes · product features that need grounded Q&A
Product
The full path from source to chatbot — productized.
Speed and simplicity beat flexibility for v1. Advanced knobs can come later. What you need now is a working loop you can trust.
Zero-config pipeline
Upload or paste sources and get a working chatbot path without assembling LangChain, a vector store, and chat UI by hand.
Grounded answers
Responses stay tied to your corpus. Citations make it obvious what the model is using — signal over hallucination theater.
Builder-first workspace
Projects, sources, indexing status, and chat testing live in one surface designed for developers who ship under time pressure.
Multi-source direction
Start with text today. Expand toward PDFs, spreadsheets, websites, Notion, Drive, and GitHub without re-architecting the product path.
How it works
Six steps. One product. No stack assembly.
From ingest to respond, Rafay keeps the pipeline visible so you always know what is indexed and what grounded the answer.
01
Ingest
Paste text or connect docs, repos, and tools.
02
Chunk
Split sources into retrieval-ready segments.
03
Embed
Index meaning so answers stay grounded.
04
Index
Build a searchable knowledge layer per project.
05
Retrieve
Pull the right excerpts for every question.
06
Respond
Ship a testable chatbot with citations.
Evidence over hype
Answers you can audit.
Rafay is built for people who write and ship code. Every chat turn should point back to source material — not a confident shrug.
- Project-scoped sources and indexing status
- Grounded responses with citation chips
- A test surface before you embed anywhere
Activation loop
Create a project
Name the knowledge surface you are testing.
Add a source
Paste the docs that should ground the bot.
Index & chat
Ask a real question and inspect the citation.
FAQ
Straight answers for builders.
Who is Rafay for?
Developers and technical founders who need grounded Q&A over docs, repos, or internal knowledge — and refuse to own the full RAG stack on day one.
What can I try in the MVP?
Create a project, add pasted text sources, index them, and chat with grounded answers plus citations. File uploads and deeper connectors expand from there.
Do I need to stitch my own infra?
No. Rafay owns ingestion through chat test so you can validate retrieval quality before investing in custom pipelines.
Is this another black-box AI chatbot?
No. The wedge is grounded answers from your sources with visible evidence — not open-ended generation without context.
Make RAG accessible
Production-ready RAG. In minutes.
Stop stitching frameworks. Start with a project, index a source, and validate grounded chat in one session.