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The platform

Discover. Authorize. Prove.

One evidence graph behind every agent action: who is acting, whether the action is justified, and a receipt for what happened.

Who is acting, and with what?

qbrinEvidence graphWhoPeopleWhoAI agentsWithMCP toolsWithAPI keysWithSaaS appsReachData storesReachReposReachInboxes
01

Discover

Give every agent an identity

Register the agents, tools and credentials in your stack, and see what each one can reach before it takes its first action.

  • An identity and an owner on every agent
  • Every tool and credential mapped to the agent that holds it
  • A reasoning trace for every run
How agents are governed
02

Authorize

Verify before AI acts

Each proposed action is checked against current evidence, permissions and company policy. The answer is ALLOW, HOLD or ESCALATE.

  • Evidence freshness and bindings verified
  • Prompt injection filtered before a tool runs
  • Tools fail closed without a valid grant
Try the authorization API
03

Prove

Leave a receipt for every decision

Approved actions carry an expiring signed grant, and every decision lands in a tamper-evident audit trail you can replay.

  • HMAC-SHA256 signed action grants
  • Tamper-evident audit log of allow, hold and escalate
  • Human sign-off on high-risk steps
Read the security model

See it move

The whole platform in one short loop.

The wheel, the authorize gate, the decision engine and the rotating headline, recorded from the sections on this page.

Illustrative walkthrough with sample data. No audio.

Play the film

Beyond people

You secured your people. What about your agents?

Three questions every team running AI agents ends up asking, usually after something has already happened.

01

Which agents exist, and what can they touch?

Agents show up faster than anyone registers them. Qbrin gives each one an identity and an owner, and maps the tools and credentials it can reach.

OwnerAgentToolProd dataHOLD
02

What do they inherit from whoever launched them?

An agent that borrows a person’s login inherits everything that login can do. Qbrin scopes what an agent may do to the action in front of it.

Claim
refund_payment
Evidence
invoice total
Verdict
HOLD claim not supported
03

Was the action actually justified?

Logs tell you what happened after the fact. Qbrin checks the claim against current evidence before the tool runs, and holds what the evidence does not support.

Inside the platform

Three moves between an agent and your tools.

01 · Discover

Every agent gets an identity. Nothing runs anonymously.

Register the agents, tools and credentials in your stack and see what each one is allowed to reach, before it takes its first action.

  • An identity and an owner on every agent
  • Every tool and credential mapped to the agent that holds it
  • A reasoning trace for every run
Explore agent governance

Agent inventory · Illustrative

AgentOwnerToolsStatus
refund-agentfinance-opsrefund_paymentGoverned
support-triagesupporttag_ticketGoverned
data-sync-botplatformexport_rowsHeld
research-agentgrowthweb_searchGoverned
onboarding-agentpeople-opscreate_accountNew

02 · Authorize

Verify before AI acts.

Every proposed action is checked against current evidence, permissions and company policy. Decisions come back in low milliseconds as ALLOW, HOLD or ESCALATE.

  • Evidence freshness and bindings verified
  • Prompt injection filtered before a tool runs
  • An expiring signed grant; tools fail closed without it
Try the authorization API
POST /v1/authorizeIllustrative
ALLOWHOLDESCALATE

The AI wants to

refund_payment

  • Order found in billing system
  • Amount within what was paid
  • Inside the refund policy
ALLOWgrant · qg_9f2c…e71a · expires in 60s

03 · Prove

Every decision leaves a receipt.

Approved actions carry a signed grant. Every decision, allowed, held or escalated, lands in a tamper-evident audit trail you can replay.

  • Tamper-evident audit log of every decision
  • Human sign-off on high-risk steps
  • Replay any run from its trace
Read the security model
Audit trailIllustrative
Chain verified4 entries · 0 gaps
  • 10:41:07refund_payment ₹4,200ALLOW#a91f…3c2e
  • 10:41:33refund_payment ₹95,000HOLD#c40b…77d1
  • 10:42:10delete_customer_recordsESCALATE#5e08…0aa9
  • 10:42:58send_email → customerALLOW#2b7d…91f4

Platform tour

Follow one action from registration to receipt.

A four-step walk-through of what happens when an agent tries to refund a customer.

  1. 01Register the agent

    Give it an identity, an owner and only the tools it needs.

  2. 02It proposes an action

    The agent reasons about the task and submits the action before any tool runs.

  3. 03The gate decides

    Evidence, authority and policy are checked. The answer is ALLOW, HOLD or ESCALATE.

  4. 04A person signs off, and it is logged

    High-risk steps route to an owner. Every outcome lands in the audit trail.

Step 1 of 4 · Register the agentIllustrative
Agent
refund-agent Governed
Owner
finance-ops
Credential
scoped key ••••8f2a
Tools
orders.lookup refund_payment
Sandbox
on
Trace
every run recorded

Register the agent with an identity, an owner and only the tools it needs. When it proposes an action, the gate checks evidence, authority and policy; high-risk steps route to an owner, and every outcome lands in the audit trail.

See it on your own agents

The decision engine

Four checks, one verdict, no guessing.

Before a tool runs, the gate asks four questions. An action only gets a grant when every ring is full. Anything short of that is held or sent to a person.

MatchesMismatchIrreversibleInjection

Illustrative. Fill shows how much of each requirement the action meets.

  • Evidencefresh, bound to the claimOrder and invoice match the claim
  • Authoritywho is askingRequester verified
  • Policytool boundaryInside the refund policy
  • Injection screeninstructions in the dataNothing injected
ALLOWrefund_payment ₹4,200

The trust layer, in motion

One verification layer for every domain.

Your agent asks. qbrin pulls the evidence from your live systems and knowledge, verifies every claim against it, and only then lets the answer through — the same way whether it’s a security alert, an outage, a mission decision, or a payment. Verified decisions, or an honest “not enough evidence”, never a confident guess.

  • Cybersecurity
  • IT operations
  • Defence
  • Space & aerospace
  • Finance
  • Healthcare
  • Legal
  • Manufacturing

0/500

fabrications on nonexistent-entity traps

LlamaIndex: 11 · naive RAG: 155+

93.7%

precision when answering

LlamaIndex: 84.7% · naive RAG: 80.5%

88%

recall@20 on the retrieval bench

bge-m3 dense

74.9%

answer coverage

It abstains on roughly 1 in 4 answerable questions rather than guess.

Held-out HotpotQA benchmark, N = 1,350 per system, measured 2026-07-18/19. qbrin is not zero-wrong: 33 wrong of 524 answered. See the full benchmarks.

Verify before AI actsrefundsemailsdeploysdeletespays

One gate at the tool boundary. Evidence, permission and policy, checked before anything runs.

Qbrin Labs

Measured, not claimed.

Every number here comes from a committed benchmark, with its sample size and its caveats. Where we do not lead, the write-ups say so.

0

Made-up answers

Across 120 trap questions on four corpora, Qbrin declined, or corrected the false premise with the cited real fact.

86%

Citations you can trust

of the sources Qbrin cites genuinely back the answer, with its double-checking on. Plain keyword search manages 6%.

100%

Always the current fact

Right after a fact changes, Qbrin gives the new value. A popular open-source memory tool gave the old one 3 times in 4.

88%

Finds the right source

of the time Qbrin finds the right source document before it answers.

See it answer

See it answer your hardest question.

Bring one real question your team keeps re-asking. We’ll connect a source, read-only, and show you the answer, sourced, in seconds, in a 20-minute walkthrough. Nothing changes in your tools.

  • One source connected, read-only
  • Your real question answered, with sources
  • Nothing changes in your tools