Why we built Uvy

Every company we talk to ships code daily and gets penetration tested once a year. The numbers do not even pretend to line up: by the deployment-frequency metric that DORA's research tracks, the fastest engineering teams ship many times a day, while the security report that lands in the spring says nothing about the code shipped in the summer. That gap, between how fast you build and how rarely you are tested, is not a minor inefficiency. It is precisely where breaches live.

A pentest is a sample, not a sweep

A human team has a fixed number of hours, so they test a slice of your surface and extrapolate from it. This is not a knock on the testers; it is arithmetic. The vulnerability that actually gets exploited is usually in the part nobody had time to reach, the third admin panel, the legacy endpoint, the integration added after scoping closed. The economics of human-only testing force a trade between depth, coverage, and cadence, and the budget only ever lets you pick one.

Most of the work is mechanical. The judgment is rare.

Recon, enumeration, fuzzing, payload generation, chaining the obvious into the dangerous, that is the bulk of the labor in a pentest, and it is exactly the kind of exhaustive, repetitive work that does not need a scarce expert doing it by hand. The rare and genuinely expensive thing is judgment: knowing what is truly dangerous, what is acceptable risk, and when to stop and escalate. That is what a human's attention should be spent on, not grinding through every endpoint one request at a time.

Proof is the whole product. Everything that comes before the working exploit is just a list of things that might be true.

So we built Uvy: an AI-native penetration testing firm that attacks like an adversary, attaches a working exploit to the findings that warrant one, and writes the audit-ready report, on every release, in isolated infrastructure. It reasons about your business and allocates effort the way an expert would, decides what is genuinely dangerous rather than grinding blindly, and carries context forward from one engagement to the next, so each test starts smarter than the last instead of from zero.

We did not build this because testing was broken. We built it because the economics that shaped testing, the assumption that every hour of it had to come from a scarce human, finally stopped being true. Once that breaks, testing on every release stops being a luxury and becomes the obvious default.