Fluree
Data + Context for AI and Apps
391 followers
Data + Context for AI and Apps
391 followers
Fluree is an enterprise AI data platform and verifiable knowledge graph database that combines immutability, security, and semantic graph capabilities. It operates as a unified intelligence layer, helping organizations connect fragmented information into a single "source of truth" to power zero-hallucination AI agents, analytics, and decentralized data sharing
This is the 3rd launch from Fluree. View more
Fluree AI
Launching today
Fluree AI gives every app and AI agent the same trusted context from your company data. Ask questions and get cited, verifiable answers from one live data layer, with permissions checked on every request. Instead of rebuilding prompts or relying on RAG guesses, Fluree queries structured data directly and connects to MCP-ready agents, dashboards, and apps in minutes.
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How much effort does integration usually require for existing business applications? Ready connectors could shorten implementation time for customers.
Fluree
@alheri_murya Integration is quite easy - we have existing data models loaded up for common business apps, which means all you have to do is point a SaaS or Google Drive or Iceberg table (etc.) at Fluree and we map it to an ontology (an intelligent shared schema of concepts). So the integration work is up front but done-for-you.
I've dealt with RAG systems that just guess at context. The idea of querying structured data directly instead feels like a real fix.
Fluree
@david_grunwald1 thanks David, we absolutely agree.
We don't want the responsibility of correctness/accuracy/precision on the LLM, we want it on the data itself. If the data is correct, the answer will be correct every time.
RAG works fine for things like blog posts, where some variation is normal. But real business data can't tolerate that inconsistency. We want the LLM to have the data model and all the structured, clean data up front — ready for direct queries, never a black box.
And if Fluree AI doesn't have access to the data, it won't invent things. In my experience, that's where things really go off the rails in agentic systems :)
how's pricing structured?
Fluree
@colton_hayes2 great question! We have a generous free tier and pricing is pay-as-you-go. Here's the cool thing: because Fluree AI runs serverless, there is zero idle cost to you. That truly means pay-as-you-go!
Our pricing unit is called "fuel." Fuel is combined compute + storage + LLM calls.
This feels like a direct answer to the RAG guessing problem I keep running into. My question is how fast the live data layer updates when source systems change. Real accuracy matters more than most teams admit.
I've watched a few AI agent rollouts stall because every app needed its own context setup. If one live data layer can actually serve dashboards, apps, and agents consistently, that's a real infrastructure win, not just a feature. I'd want to see how it handles schema changes over time though.
I've been part of two failed AI agent pilots, and both died because nobody trusted the answers enough to act on them. Cited, verifiable responses solve the trust problem directly instead of hoping people believe the output. If Fluree can keep that reliability while scaling across multiple apps, I think it solves a problem most vendors talk around.