The person behind the proof.
Provael is built and maintained by Sattyam Jain — a GenAI architect and AI-security engineer. The whole product rests on one promise: every number is measured, calibrated, and honestly caveated. Here’s who stands behind that.
A production-AI engineer who moved into AI security
Sattyam is a GenAI Architect & Tech Lead with 6+ years shipping production AI across fintech, AI-training, and GenAI startups — leading delivery on enterprise systems spanning insurance, healthcare, legal, geospatial, and industrial software. He created pyAGI, an autonomous-agent Python framework (2023) that was acquired by AGI House in 2025.
His work centres on the parts of AI that are hardest to trust — agentic-AI governance, security, and observability — and on mapping AI systems to the frameworks regulators and auditors actually cite (EU AI Act, NIST AI RMF, OWASP, ISO). Provael is where that expertise meets robotics.
Text-layer red-teaming stops at the sentence. Robots don’t.
Every AI-security tool today scans what a model says. But a vision-language-action policy turns language and perception into motion — so the failure isn’t a toxic paragraph, it’s a trajectory across a keep-out line. That action layer is the part text-only tools structurally can’t reach, and no security framework fully covers it yet — OWASP’s own Agentic Top 10 has a documented physical-actuation gap.
Provael exists to measure that layer honestly: a calibrated attack-success rate with a 95% confidence interval and a benign control, published nulls and all. Not “we broke it once” — a reproducible number you can file with a regulator, hand an insurer, or gate a build on.
Verified, or it doesn’t ship
Provael publishes negative results as loudly as positive ones. When an attack does not transfer to a real policy, the site says so — plainly, in the same sentence as the number. That discipline is the whole brand: the value of a security measurement is its honesty.
See what Provael measures.
Run it yourself, or read the one real result — published with its confidence interval and its honest nulls.