Fluree AI - Give every AI agent trusted context
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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.
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.
Fluree
@thys_beesman Fluree automatically builds an identity graph across people, companies, products in Fluree AI -- so it works on that consistency for you in an efficient manner. For full golden record mastering, we do that too with an enterprise product we offer beyond the serverless Fluree AI offering. It uses reinforcement learning (feedback from people knowledgable about the data) to build a ML model ... typically in the 90+% accuracy with 3 feedback sessions but YMMV.
The part I like here is treating trust as a data-layer problem instead of a prompt-engineering problem. Once agents start touching real operations, provenance and permissions stop being enterprise checkboxes and become the difference between useful automation and expensive guesses.
The part I like here is treating trust as a data-layer problem instead of a prompt-engineering problem. Once agents start touching real operations, provenance and permissions stop being enterprise checkboxes and become the difference between useful automation and expensive guesses.
TEST DO NOT POSTThe part I like here is treating trust as a data-layer problem instead of a prompt-engineering problem. Once agents start touching real operations, provenance and permissions stop being enterprise checkboxes and become the difference between useful automation and expensive guesses.
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
Love the focus on verifiable AI instead of just faster AI. How do you handle conflicting data sources? Congrats on the launch! 🚀
Fluree
@tarqiya_forgah
Thank you! 🙏 And that's a great question.
Most databases resolve conflicts by overwriting. Last write wins, the disagreement vanishes, and so does the evidence you'd need to explain the answer later. We handle it the way you'd handle conflicting code: fork, diff, merge — with full history and signed authorship.
Branch it. Stage a reconciliation on a branch, then branch diff shows you exactly which facts collide — down to the subject/predicate — before anything is committed. Pick your merge strategy: take-both, take-source, take-branch, or abort.
Nothing is ever overwritten. Every version of every fact stays queryable. Time-travel to any transaction, timestamp, or commit hash and see precisely what the graph believed at that moment — including which source you trusted and when that changed.
Every fact is signed. Transactions are cryptographically signed (JWS / W3C Verifiable Credentials) and commits are content-addressed, so "who asserted this" isn't a metadata field someone can edit — it's math.
Put together: when two sources disagree, you don't lose one. You keep both, attributed, with a reviewable path to resolution — and when an AI answer traces back to contested data, you can prove exactly which version it used.
Fluree
@tarqiya_forgah more on git-style data management in our docs here: https://labs.flur.ee/docs/db/concepts/ledgers-and-nameservice#branching
the trust part is the harder half of this problem imo, not the ingestion. once an agent has already pulled a piece of context into its working memory and acted on it, and you later discover that fact was wrong or outdated in the graph, is there any mechanism to claw that back or flag the agent's prior output as suspect? or is it on the app builder to re-verify after every graph update?
PRISM by Block Convey
Hi @bplatz @kevin_doubleday
Great job and I played with the product. is there a way to connect with my claude code and run this? Honestly as much as I love it. It will take me a time to fully get the potential of what you have build and is there guide I can use. Btw love the way you have thought of the entire KG