The company brain for every AI you use
Use 20 AI tools and all 20 want your data. qbrin is the one brain they go through. It knows your company, checks every request, and gives each tool only what the task needs.
Drive and GitHub are read once, read-only, into one living memory. From here on, tools ask the brain instead of your apps.
Maya, in Slack, asks what was decided about billing. She gets a cited answer, built only from what she can already see.
A coding AI asks for the whole repo. It gets the three files the task needs, and nothing else.
A support agent wants to refund ₹95,000. No finance approval is attached, so the refund waits.
Ravi, on WhatsApp, asks about the leave policy in Telugu. The answer comes back in Telugu, with the HR policy linked.
A chat AI asks for all of Drive to answer one question. That is far more than it needs, so a person decides.
Your own code-review agent comments on a pull request. It is within policy, so it goes ahead, and the receipt is kept.
Plugs into the tools you already use. Gmail, Slack, Drive, Notion, Jira and more every week.
Why one brain
Every AI tool you add wants its own connection to Drive, Slack, Jira and the rest, and pulls in everything it can reach. qbrin turns that sprawl into one brain with one gate.
How it works
The three steps around the ring. They run, in that order, for every question, every AI tool and every agent.
01Know
qbrin reads the tools you already use, once and read-only. It remembers people, projects and decisions, and who is allowed to see what.
02Authorize
People, AI tools and agents ask the brain, not your apps. Each request is checked against permissions, policy and proof, and gets only what the task needs.
03Govern
Every person, AI tool and agent, what each can reach, what it touched, and what is waiting for a yes. Every decision keeps a receipt.
Waiting for a yes
Refund ₹95,000 · Support agent
No finance approval attached
ApproveDecline#a91f…3c2eCoding AI · 3 filesALLOW#c40b…77d1Maya · 2 sourcesALLOWBring your own agents
Most teams already run agents, like a code-review bot on every pull request. Keep them. They ask qbrin for context and for permission, like everything else.
Need more? Run ours on the qbrin harness, each with an identity, its own rules and a sandbox.
Meet the qbrin agentsYour agents and AI tools
qbrin agents · on the qbrin harness
Early accessIdentity security
Every identity, every cloud
Connect AWS, Google Cloud, Entra ID, Okta, GitHub and Google Workspace read-only. qbrin builds one graph of every person, service account, key and AI tool, follows each session across them, and tells you what to fix first.
dana@northwind.healthOkta · AWS · GitHub, linked by verified email and SAML
09:12OktaSign-in, new device, push approvedALLOW09:13AWSAssumeRoleWithSAML → prod-adminALLOW09:21AWSCreateAccessKey for ci-deployHOLD09:24GitHubPersonal access token created, org-wide scopeESCALATESession riskBehavior · Likelihood · Impact
A provider that cannot be read makes the score cannot score, never low.
The same person linked across Okta, AWS, Entra, GitHub and Google, with the reason for every link. Nothing is merged on a display name.
A sign-in, the role it assumed and what it did next, read as one session across providers.
47 rules mapped to MITRE ATT&CK, from root sign-ins to LLM-key abuse, beside the standing fixes: stale keys, admins without MFA, unused access.
Reversible actions only, dry-run first. Never yourself, never the last admin, and a second admin approves the action and any rollback.
Read-only by default. Alerts go to a webhook, Slack or Splunk. A tool that holds its own grant to your systems is shown as remaining exposure, not as protected.
The console
See who and what can reach your company’s knowledge, what each one touched, and what is waiting for a yes. Click through three screens.
app.qbrin.com/identities
app.qbrin.com/work/approvals
app.qbrin.com/admin/audit
Measured against the industry
Every number comes from a run we keep, with its sample size and its caveat. We publish the runs where we don’t lead, too.
20×
Measuredfewer made-up answers than traditional RAG
Made-up answers when the answer isn’t in the dataLower is better
301 questions with no answer in the data, same answer model. Hybrid RAG: 18%.
0
Measuredmade-up people in 500 trap questions
Questions about people who don’t existLower is better
Same retrieval budget. qbrin isn’t zero-wrong overall: it is right 93.7% of the time it answers, and declines about 1 in 4 rather than guess.
~9×
Measuredless to read for every answer
Tokens read per answerLower is better
Decision questions. A short briefing instead of a pile of chunks: faster and cheaper answers.
86%
Measuredof citations actually back the answer
Cited sources that support the claimHigher is better
Both find the right source most of the time; only qbrin checks that the source says what the answer says.
100%
Measuredcurrent, right after a fact changes
Gives the new value after an updateHigher is better
A popular open-source memory tool gave the old value 3 times in 4.
6.8×
Measured vs publishedpreferred over GPT-4o on company questions
Answers preferred over GPT-4oHigher is better
Glean’s number is self-published on its own data: same measure, different data. 42 questions, one AI judge; a stricter rerun is in progress.
Verify before AI acts
Before an agent takes an important action, Qbrin checks whether the action is actually justified by current evidence, permissions, and company policy. It then returns ALLOW, HOLD, or ESCALATE.
Even simpler: Qbrin is a safety layer between AI agents and the tools they use. It checks whether an action has enough proof and permission before letting it happen.
curl -X POST https://app.qbrin.com/v1/authorize \
-H "Content-Type: application/json" \
-d '{
"action": "refund_payment",
"args": {
"paymentId": "PAY_123",
"amount": 48500,
"currency": "INR"
},
"evidence": [
{
"kind": "customer_request",
"value": "Customer refund request of INR 48500 for PAY_123 approved by Finance"
},
{
"kind": "refund_policy",
"value": "Policy 2026: full refunds allowed within 30 days"
}
]
}'Sample response
authority: "caller"). A gated action like refund_payment needs evidence attested by a system of record. The sandbox runs in shadow mode: nothing is enforced and no production grant is minted.Community & launch reviews
Unedited reviews and discussion from our Product Hunt launch and PeerPush community rating. Click any review to verify the original live thread.
This product beats all competitors in benchmarking! Proven trust layer architecture where citations actually support claims.Bonan KouAI Researcher & MakerProduct HuntVerified feedback
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.Eymen YağbasanProduct LeadPeerPushTrending now #1
Awesome! The trust layer architecture and enterprise memory for AI agents is a game changer for high-stakes workflows.Enterprise Security LeadCritical InfrastructureProduct HuntVerified feedback
That’s the right framing — provable freshness rather than just a policy statement. This is a genuinely solid answer, most tools I’ve seen in this space just wave their hands at ‘we respect permissions’ and stop there. Following the launch.Gal DayanFounding EngineerPeerPush5.0 / 5.0 rating
The provenance, permissions, citations, and abstention behavior solve real hallucination risks in high-stakes environments where one wrong answer creates serious consequences.Founder & AI ArchitectCritical Ops & AIProduct HuntVerified feedback
The abstention behavior here is a really thoughtful design choice, so many RAG setups will confidently fabricate instead of admitting uncertainty.EylülAI Engineer
POST https://app.qbrin.com/v1/authorize needs no key. It runs in shadow mode, so nothing is enforced and no real grant is issued. The docs show a copy-paste example.Built for teams running many AI tools
Bring one question your team keeps re-asking, and one action you would worry about letting an AI take. In a 20-minute walkthrough we show qbrin on a real example from your setup. Nothing changes in your tools.
or email hello@qbrin.com