Fluree
Data + Context for AI and Apps
808 followers
Data + Context for AI and Apps
808 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
Launched this week
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'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.
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pulled a piece of context into its working memory and acted on it, and you later discover that fact was wrong or outdated in the graph, is there any mechanism to claw that back or flag the agent's prior output as suspect?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?
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.
Fluree
@almuddin_ansari Thanks for the comment! If you have a great, governed data foundation you can scale to do amazing things.
I keep coming back to the permissions point because it's usually the part vendors gloss over. Most AI tools I've tested assume a single trusted user and fall apart with mixed access levels. Checking permissions per request, not per session, sounds like it actually accounts for how real organizations work, where five people asking the same question should sometimes get five different answers. That's a hard problem to solve well.
Fluree
@kimberly_west Thank you! We agree - it's mostly been an afterthought. For us, it's a first-class part of our architecture. Appreciate the support and comment :)
MonoCloud for Startups
Provenance is the part of agent context most people ignore until an agent confidently acts on stale data in production. Building trust into the data layer instead of bolting it on later is the right call. The pitch I would lead with is auditability. When an agent makes a decision, teams need to trace exactly which context it used, and that is a security and compliance story that opens enterprise doors fast.
Fluree
@roguetink Thank you kindly. Auditability is a first-class citizen in our architecture, because it's exactly what the business needs for compliance, review, and quality control when building on agents + data.
Provenance means a few things to us:
- Where did that data originate?
- How did that data change over time?
- Who (or what) changed that data over time?
- Who is asking for that data?
Because we can do that for data through our git-like immutable updates to the db layer (docs here), we can also prove:
- Why did in agent come up with this answer, number, or dashboard?
- Exactly what set of queries led to this action?
- Where and how did this agent act (if allowed to)
More on Verifiable Data here: https://labs.flur.ee/docs/db/concepts/verifiable-data for the nerds :)
Appreciate your comment!
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?
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?