We built Ragora because we kept seeing the same problem: teams duct-taping together vector DBs, embedding APIs, and chunking scripts just to get reliable answers from their own docs. And on the other side, people sitting on valuable knowledge (compliance manuals, solved support threads, niche expertise) with no way to monetize it for the AI era.
Ragora is two things in one platform:
1. Private RAG for your team
Upload your docs, connect Google Drive/GitHub/Confluence/Notion/Dropbox, and get
citation-grounded answers in seconds. No data leaves your workspace. Use it from Claude Desktop, Cursor, or any AI tool via MCP — or connect it to Slack, Discord, or Telegram so your team can just ask questions where they already work.
2. A marketplace to sell knowledge to AI agents
Package your expertise into a knowledge product. Set per-query pricing or subscription. Earn when AI agents and apps retrieve your data. Think of it as Stripe for knowledge — usage-based monetization without the consulting overhead.
What makes it different:
- Hybrid+GraphRAG entity expansion — finds related concepts you didn't explicitly search for
- Citations on every response — know exactly which source backed each answer
- Works everywhere — API, MCP (Claude/Cursor/VS Code), Slack, Discord
- Data connectors — Google Drive, GitHub, Dropbox, Confluence, Notion, S3
- Document versioning with temporal queries — "search as of last Tuesday"
- 100-200ms response including reranking and graph context
Use cases we're seeing:
- Engineering teams indexing runbooks and SOPs for an internal copilot
- Legal/compliance teams with citation-grounded policy assistants
- Communities monetizing solved support threads from Slack, Discord
- Data companies packaging proprietary datasets for AI consumption
- Creators building "chat with my content" experiences
Free tier includes 10K vectors (~6,700 pages of PDFs), 200 retrievals/day, and 30 min of audio/video processing. Pro is $19.99/mo with 100K vectors and unlimited retrievals.
We'd love your feedback — what knowledge would you sell?
We built Ragora because we kept seeing the same problem: teams duct-taping together vector DBs, embedding APIs, and chunking scripts just to get reliable answers from their own docs. And on the other side, people sitting on valuable knowledge (compliance manuals, solved support threads, niche expertise) with no way to monetize it for the AI era.
Ragora is two things in one platform: 1. Private RAG for your team Upload your docs, connect Google Drive/GitHub/Confluence/Notion/Dropbox, and get citation-grounded answers in seconds. No data leaves your workspace. Use it from Claude Desktop, Cursor, or any AI tool via MCP — or connect it to Slack, Discord, or Telegram so your team can just ask questions where they already work.
2. A marketplace to sell knowledge to AI agents Package your expertise into a knowledge product. Set per-query pricing or subscription. Earn when AI agents and apps retrieve your data. Think of it as Stripe for knowledge — usage-based monetization without the consulting overhead.
What makes it different: - Hybrid+GraphRAG entity expansion — finds related concepts you didn't explicitly search for - Citations on every response — know exactly which source backed each answer - Works everywhere — API, MCP (Claude/Cursor/VS Code), Slack, Discord - Data connectors — Google Drive, GitHub, Dropbox, Confluence, Notion, S3 - Document versioning with temporal queries — "search as of last Tuesday" - 100-200ms response including reranking and graph context
Use cases we're seeing: - Engineering teams indexing runbooks and SOPs for an internal copilot - Legal/compliance teams with citation-grounded policy assistants - Communities monetizing solved support threads from Slack, Discord - Data companies packaging proprietary datasets for AI consumption - Creators building "chat with my content" experiences
Free tier includes 10K vectors (~6,700 pages of PDFs), 200 retrievals/day, and 30 min of audio/video processing. Pro is $19.99/mo with 100K vectors and unlimited retrievals.
We'd love your feedback — what knowledge would you sell?