About
The team behind qbrin.
qbrin is built on one conviction: an enterprise AI answer you cannot verify is a liability wearing a confident voice. So we built the layer that verifies.
Why
Why qbrin exists.
Every enterprise AI search product promises answers from your company’s knowledge. Almost none of them will tell you when the answer is not there. The result is the most expensive failure mode in enterprise AI: an answer that looks right, reads confidently, cites something, and is wrong.
qbrin is built around the opposite discipline. Every answer must be grounded in citable sources from your own data, every claim passes verification gates before you see it, and when the evidence is missing, qbrin says so instead of guessing. Cited, or abstain. We think that trade, occasionally hearing “not enough evidence”, in exchange for never being confidently misled, is what makes AI usable for work that matters.
qbrin is the trust layer for people and AI agents. The discipline that governs our answers, cited or abstain, extends to what your AI can reach and do: Know every identity that can act, Scope each AI to the data its job needs, and Check each action before it happens. Give your AI the task, not your company.
Method
How we work.
We publish our measurements, and we show the method. The engineering blog documents how we score the trust layer: adversarial trap questions, audited scoring, and honest reporting of the costs as well as the wins. When a benchmark result flatters us but the method is weak, we say so. When abstention rejects a true answer, we count it and publish the rate.
That is also the standard we hold this website to: the benchmark numbers are scoped to the suites they came from, and we have never implied a head-to-head result we did not run.
Founder
Who builds it.
qbrin is built by Kate Sai Kishore. His background is security engineering, including identity security at Okta, the discipline where you assume every input is hostile and design so that trust is earned by verification, never by confidence. qbrin applies that same posture to enterprise AI answers.
Before qbrin, Kishore built and shipped consumer products end to end, and he writes the measured write-ups on the qbrin blog.
Contact.
The fastest way to reach the team is hello@qbrin.com, a human reads every message. You can also connect directly with the founder on LinkedIn, book a walkthrough, or test qbrin on your own workspace.