Fluree
Data + Context for AI and Apps
650 followers
Data + Context for AI and Apps
650 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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Launch Team / Built With
How do developers measure the accuracy improvements of applications built on Fluree? Cutomers case studies with measureable outcomes would answer that question well.
Fluree
@advin_jadis We have case studies on our website, but generally testing is best with your own use case and is pretty straight forward. Connect your relevant data with Fluree AI, use it or expose as MCP if similar/same data is in other places and you want to test against 10->20 questions. In this webinar we recently did you can see a side-by-side real-time example of Fluree data vs Snowflake MCP -- close to 10x better in Fluree across the board in speed, lower token usage, accuracy.
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.
The strongest part here is putting provenance into the context layer instead of leaving it for dashboards after the fact. One edge case I would want to understand: when two source records conflict, does Fluree expose both to the agent/user, or resolve them into one trusted context before the model sees it?
Fluree
@yaroslav_stelmakh For People, Companies, Products -- the 80% rule of what you want to merge, we expose the identity graph to the AI but you could choose not to. We find it helps for most use cases and allows the AI to explore match probabilities and do disambiguation on it is own if needed.
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 :)
The "zero hallucination" claim is worth scrutinizing. Grounding AI in a verifiable knowledge graph genuinely reduces confabulation, but it doesn't eliminate it,
the retrieval layer still has to correctly map a query to the right subgraph, and that step can fail quietly.
Fluree
@mohammed_messeguem Sure, and the AI model you use here can also make a big difference. But efficiently allowing the model to understand the knowledge that exists, how it is connected across graphs, and statistics about the data -- combined with a good AI model works incredibly well.
I really like that you led with trust and permissions rather than just speed. That is the part most teams bolt on later and end up regretting, and putting it first says a lot about how you think about this. From my own time trying to get dependable answers out of messy data, the moment that always wins people over is when a tool can simply say "I do not have that" instead of guessing. If Fluree does that gracefully, you are going to earn a lot of trust quickly. Congrats on the launch.
Fluree
@cmumulle Thank you so much Christian. YES, absolutely. Because these aren't probablistic guesses, Fluree will always return "I don't have that," instead of inventing an answer. It will even be honest when their is a partial answer.
Trust is the final frontier in our opinion and something we have been working on for a long, long time.
To prove that, here is a pre-AI blog post we wrote on "data-centric trust" - https://flur.ee/blog/data-centric-trust
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.