The team behind qbrin

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.

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.

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.