This product was not featured by Product Hunt yet. It will not be visible on their landing page and won't be ranked (cannot win product of the day regardless of upvotes).
qbrin
Enterprise AI trust layer with citations & 20x fewer tokens
Qbrin is a trusted enterprise layer for AI agents, built for companies that cannot afford wrong answers. It turns messy company knowledge from documents, Slack, Gmail, tickets, wikis, databases, and systems into structured, evidence-backed memory with permissions, provenance, citations, freshness, relationships, and abstention. For high-stakes teams in defense, medicine, drones, security, finance, and compliance, Qbrin helps AI agents know what to trust, when to answer.
I built Qbrin, a universal trust layer that connects your company's knowledge to AI agents. It answers with proof, respects permissions, and refuses to guess when unsure. Try it now for free!
citations + 20x fewer tokens is the enterprise combo everyone wants 👏 landing any big logos yet?
the permission-respecting part is what I'd actually want to stress test. Access isn't static - someone leaves a team, a channel gets locked down, a doc's sharing changes. If a document was ingested when a user had access and their permissions get revoked later, how fast does that propagate to what the agent will surface to them? That's usually the gap between "respects permissions" in theory and in an actual audit.
how does it handle data freshness when a source like slack or a wiki changes constantly does it re-embed in real time or batch and how much lag should i expect before an agent reflects the latest info
Tested it with our messy internal docs and the citations actually trace back to the right Slack thread, which is rarer than it should be. The abstention behavior when it lacks confidence feels genuinely useful for high-stakes workflows.
the abstention behavior here is a really thoughtful design choice, so many RAG setups will confidently fabricate instead of admitting uncertainty.
How are you claiming that its gives only right answers? And dont you think the other products do the same mean the RAG systems?
About qbrin on Product Hunt
“Enterprise AI trust layer with citations & 20x fewer tokens”
qbrin was submitted on Product Hunt and earned 40 upvotes and 19 comments, placing #15 on the daily leaderboard. Qbrin is a trusted enterprise layer for AI agents, built for companies that cannot afford wrong answers. It turns messy company knowledge from documents, Slack, Gmail, tickets, wikis, databases, and systems into structured, evidence-backed memory with permissions, provenance, citations, freshness, relationships, and abstention. For high-stakes teams in defense, medicine, drones, security, finance, and compliance, Qbrin helps AI agents know what to trust, when to answer.
qbrin was featured in Productivity (655.7k followers) and Artificial Intelligence (473.1k followers) on Product Hunt. Together, these topics include over 250.1k products, making this a competitive space to launch in.
Who hunted qbrin ?
qbrin was hunted by Kate Sai kishore. 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.
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Kate Sai kishore