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.
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.
The "single source of truth" vision resonates with anyone who's managed multiple disconnected databases. Hope this gets the attention it deserves.
One of the biggest AI challenges today is reliable context, and Fluree seems to be addressing it head-on. Excited to see how developers build on this.
Love seeing products tackle the data layer instead of just building another AI interface. The knowledge graph approach feels like a strong foundation for enterprise AI.
I might be missing it, but the workflow angle here is interesting: Fluree feels most useful when it saves a repeated setup step, not just one-off time. Curious how teams usually measure that after a week or two?
checking permissions on every request is the right call for correctness but that's a lot of round trips for an agent mid multi-step reasoning - are you seeing real latency overhead at scale, or is the permission check itself cacheable?
This is why AI hallucinates. Messy data in, and sometimes even garbage out. I love that someone is actually solving this at the source. Congratulations on your launch! :)
How does this work for someone who is not a data expert but needs their AI to stop making things up?
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
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?
Love the focus on verifiable AI instead of just faster AI. How do you handle conflicting data sources? Congrats on the launch! 🚀
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.
Congrats on the launch. The detail that stands out is checking permissions on every request instead of only at ingest. That is the failure most teams find late, when an agent summarizes something into an answer the asker was never cleared to see.
One builder question: when the graph auto classifies data and gets a relationship wrong, how visible is that to the person asking? Citations help a lot, but a confidently wrong join is harder to catch than a hallucinated sentence.
Good call on the MCP side, that is the right seam for this.
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?
About Fluree AI on Product Hunt
“Give every AI agent trusted context”
Fluree AI launched on Product Hunt on July 24th, 2026 and earned 327 upvotes and 77 comments, earning #3 Product of the Day. 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.
Fluree AI was featured in Developer Tools (516.4k followers), Artificial Intelligence (474.4k followers) and Data (2.4k followers) on Product Hunt. Together, these topics include over 187.9k products, making this a competitive space to launch in.
Who hunted Fluree AI?
Fluree AI was hunted by Ben Lang. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
Want to see how Fluree AI stacked up against nearby launches in real time? Check out the live launch dashboard for upvote speed charts, proximity comparisons, and more analytics.
Ben Lang
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.