qbrin
Qbrin: The universal trust layer for enterprise AI agents
What this is
An enterprise memory/trust layer that tracks which information AI agents can rely on and who's allowed to see it — for companies where a wrong AI answer is costly.
Our one-line summary — not the founder’s tagline.
- How it works
- Turns company knowledge from documents, Slack, Gmail, tickets, wikis, databases and other systems into a structured enterprise memory layer that tracks which information is current, its source, permissions and supporting evidence for AI agents.
- What's different
- Unlike generic chat tools or MCP-style connectors that just connect AI to data, it organizes that data with provenance and permissions so a model can refuse to answer when evidence is weak.
- Best for
- Enterprises using AI agents in sensitive environments where a wrong answer is costly.
What we measured
Nobody else publishes this — it comes from knocking on the door every day.
- Watched by us
- 18 days
- Last checked
- 2d ago
Where it shows up
Every group here is a page of its own — each one checked daily.
The numbersIs it still shipping, is it overpriced, where did it land — and what the price is built from.
League price & chart
No money. No seat. It goes on your record — and in 28 days reality settles it.
Calls are closed while we rebuild accounts. You can still read every one of them.
AI. Launched 18d ago on PeerPush, where it placed #551. Today, the site itself changed 2d ago — evidence it's still shipping, not just announcing.
Quiet for 2 days — no penalty yet, but quiet doesn't stay free.
We check this site every day, ourselves. A founder can post “still working on it” — a claim like that doesn't price. What we price is what we can verify from the outside: evidence, not announcements. The real question isn't “will this be huge?” — it's “will they still be moving in four weeks?”
Shipping record
1 separate day we saw this ship · source: page changed
Beyan değil kanıt: founder “çalışıyoruz” diye post atabilir, ama sevk ettiyse izi kalır. ⚠️ Kaydı olmayan ürün “sevk etmedi” demek değildir — o üründe izleyecek bir kaynağımız yok demektir.
The market viewHow this launch is priced and ranked in our league — the investor side of the page.
ranked by the reality anchor, not the market price
It placed #551 on PeerPush with 45 votes.
No matter how much money goes in. There is no pump here — you can't make yourself right by buying more. The line only moves on things that actually happened: an award, revenue that grew, a new platform, code that shipped — or silence.
En eski 3 hareket bu listede yok — toplam 15 olay var. 2 quiet days in between are left out — nothing happened on them. A launch that goes quiet eases down a little at a time — never a cliff you could have run from the night before.
Daily tracking just started for this launch — the first point is on the board. A second reading lands with tomorrow's run, and the curve builds from there.
How the launch is moving on its own board, day by day — the crowd's attention.
A flat line is normal: votes stop within a day or two of launch, on every board. What's unusual — and what actually counts — is a launch that keeps pulling votes long after its day is over.
About
Qbrin is a trusted enterprise memory layer for AI agents, built for companies that cannot afford to get answers wrong. Most enterprise AI tools focus on giving models access to more documents, apps, and tools. But access is not the same as trust. In sensitive environments, a model should not simply retrieve the nearest document and generate a confident answer. It needs to know which information is current, which source is authoritative, who is allowed to see it, what evidence supports the answer, and when it should refuse to answer because the evidence is not strong enough. Qbrin turns messy company knowledge from documents, Slack, Gmail, tickets, wikis, databases, and operational systems into a structured enterprise memory layer for AI agents. Unlike generic chat tools or MCP-style connectors, Qbrin does not just connect AI to data. It organizes knowledge with provenance, permissions, citations, temporal context, entity relationships, evidence paths, and abstention behavior. This makes Qbrin especially useful for high-stakes teams working in areas like defense, medicine, drones, security, finance, compliance, and critical operations, where one wrong answer can create serious consequences. These teams do not just need faster search. They need AI systems that can prove where an answer came from, respect access controls, avoid stale knowledge, surface contradictions, and say “I don’t know” when the answer is not supported. Qbrin sits between enterprise data and AI agents. It prepares company knowledge before the model sees it, routes questions through the right memory paths, retrieves evidence-backed context, and gives agents a safer foundation to reason from. The goal is not “chat with your docs.” The goal is reliable enterprise AI that can be trusted in production decisions. Our core belief is simple: in high-stakes companies, the biggest problem is not that AI lacks access to information. The biggest problem is that AI does not know what information to trust. Qbrin is built for the companies that cannot afford to go wrong.
Where it launched
1 platform| Platform | Votes | Counts toward price | Link |
|---|---|---|---|
| PeerPush | 45 | sets the price | ↗ |
The board it did best on sets the price. Every other board only adds to it if the launch also placed high on that board too — because just showing up somewhere isn't an achievement. Listing on twelve directories is free; placing well on them isn't.
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