Fluree AI

Most data is in data lakes at other orgs.

Say I have most of my data in snowflake, can fluree connect to other warehouses/lakes?

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.

It feels like we’re getting past the stage of building on top of models and starting to solve the harder problems around trusted data and context.

A graph-based approach to memory and verifiable info makes a lot of sense.

Looking forward to seeing where this goes.

Congrats on the launch 🚀
@bplatz @kevin_doubleday

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.

Interesting launch! Especially - building permissions into the query layer instead of bolting them onto the model, that's the exact spot access control leaks when it's an afterthought IMO. Do you guys surface the actual query that ran so I can catch an intent miss myself?

Hey. So If the data is correct, the output is correct" is a clean line but it quietly assumes correctness is binary, when in most real datasets it's not. A CSV can be internally consistent and still be stale, or two systems can both be technically correct about the same customer using different definitions of an active account. Structured queries against a graph solve hallucination, but they don't solve disagreement between sources about what the ground truth actually is. How does Fluree handle it when two ingested sources genuinely conflict rather than one just being wrong, since that is a different problem than an LLM making something up.
Also on the plug in any AI part, if Claude, OpenAI, Gemini, and Ollama can all reason over the same graph through MCP, does the cited and reproducible guarantee hold equally well across all of them, or does the quality of the citation depend on how well a given model respects the structured query results versus improvising around them.

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?

the "throw everything at it and the graph builds itself" part is what I keep coming back to. auto-classifying data and mapping it to an ontology is exactly the kind of thing that works great 95% of the time and quietly gets one relationship wrong the other 5%. when the automatic mapping misclassifies a field or links two different concepts together, is there a review step before that goes live, or does it just start feeding agents until someone notices an answer looks off?

Hey Product Hunt — Brian here, CEO of Fluree.The backstory:we spent years building governed, verifiable graph data infrastructure for enterprises — provenance, permissions, cryptographic audit trails, the unglamorous stuff.Then LLMs arrived, and suddenly the entire world had our problem: AI that's brilliant at language and terrible with data. Hallucinated numbers. Context that resets every conversation. Five tools, five silos, five versions of the truth.Fluree AI is our answer: an intelligence layer that sits under everything you build.Here's the flow:Throw everything at it. CSVs, databases, docs, SaaS exports. Fluree classifies your data and connects the dots automatically — the graph builds itself.Plug in any AI. Claude, OpenAI, Gemini, Ollama — any MCP-speaking agent reasons over the same graph. No tools yet? Ours is built in.Get answers you can prove.Responses are structured queries against the graph, not generated guesses — so every answer is cited, permissioned, and reproducible. If the data is correct, the output is correct.Build unlimited interfaces on one foundation. This is the part that changes how you work: ask a question, then say "make that a dashboard," then "turn it into an app my team can use," then "deploy an agent that watches this." Chats, dashboards, apps, and agents all read from — and write back to — the same governed graph. You stop rebuilding context in every tool. It compounds instead.Most software starts from the UI and traps your data underneath it. We think the future starts with the right data + context — and then any interface you want becomes cheap to build and safe to trust.Getting started takes ~2 minutes: sign up free, drop in a dataset (messy is fine — that's the point), and ask your first question. No demo call, no sales gate.I'll be in the comments all day with our engineering team. Ask us anything — and if you think "cited, verifiable answers" sounds too good, please come try to break it. Genuinely. That's the fun part.