Gradaris was founded by an enterprise technology leader with 30 years of experience across banking, retail, government, and fintech — someone who has seen first-hand what it costs when AI governance lags behind AI deployment.
Organizations are deploying AI agents faster than any governance framework can track them. Some are built by engineering teams with careful oversight. Many more are built by finance, operations, and marketing teams using no-code tools — quietly, without IT visibility, and without any audit trail.
When regulators ask which AI systems your organization operates and what controls are in place, most compliance teams cannot answer with confidence. Not because they haven't tried — but because the tools to answer that question haven't existed.
Gradaris exists to change that. We give compliance teams continuous, auditable evidence that their AI agents meet regulatory requirements — however those agents were built, by whoever built them, on whatever platform they run.
The result is a governance record your legal team can defend, your board can review, and your regulators will accept.
Governance evidence that updates with every agent run, not a point-in-time audit that's stale the moment you close the report.
SDK for engineers, webhooks for power users, plain-English registration for everyone else. Governance that meets your teams where they already work.
Cryptographically signed governance reports, EU AI Act article mapping, and a full audit log — the evidence package your legal team can hand directly to a regulator.
Gradaris was built by someone who has spent a career inside the organizations this platform serves — not observing regulated industries from the outside, but leading technology programs inside them.
Gradaris was founded after spending years inside financial services technology — specifically inside the systems that regulators scrutinize most closely. As organizations began deploying AI agents at scale, the same pattern kept appearing: the governance infrastructure was nowhere near ready.
Compliance teams were being asked to demonstrate oversight of AI systems they had limited visibility over. Audit trails were incomplete or non-existent. When regulators asked questions, the honest answer was often "we don't have that evidence yet."
That gap — between how quickly AI agents proliferate and how slowly governance catches up — is what Gradaris was built to close.
The career behind Gradaris spans 30 years across enterprise data architecture, financial systems leadership, and regulated industry operations — building and governing the technology programs that regulators scrutinize most closely.
That work has included leading enterprise-wide financial system implementations across organizations operating in more than 20 countries, spanning banking, retail, government, and fintech — environments where data integrity, auditability, and regulatory compliance were not optional features, but the baseline requirement for every design decision.
Alongside industry practice: time spent as a former adjunct professor at university level, teaching information systems and technology management — translating complex governance concepts for the next generation of practitioners.
Building an AI governance platform requires more than technical capability — it requires understanding what compliance officers actually face when a regulator asks hard questions, what evidence auditors find credible, and what "good governance" looks like in practice inside a regulated organization.
That understanding is built into every design decision Gradaris has made — from the three-tier methodology that separates verified controls from assessor judgment, to the cryptographic governance hash that makes every report tamper-evident, to the no-code registration path that ensures shadow AI gets governed, not just the agents IT already knows about.
Every Gradaris assessment includes explicit confidence levels. We distinguish between what is verified from system logs, what is measured empirically, and what involves assessor judgment. We never present uncertain findings as certainties.
Every governance report carries a cryptographic hash of the assessment methodology, input data, and scoring criteria. If any element changes, the hash changes. Governance evidence should be tamper-evident by design, not by policy.
The biggest governance gap is not the AI systems engineering controls — it is the agents everyone else built. Gradaris must work for the finance analyst who built a ChatGPT workflow, not just the Python developer who wrote a production agent.
A governance score that reflects last quarter's state is not governance — it is compliance theater. Every agent run generates telemetry. Governance evidence stays current with the system it describes.
The EU AI Act is the current catalyst. But good AI governance predates and outlasts any single regulation. The Gradaris methodology maps to multiple frameworks so your governance program remains valid as the regulatory landscape evolves.
Governance output should be designed for the moment a regulator asks for it, not retrofitted afterwards. Every Gradaris report is structured to answer the questions regulators actually ask, in the format they expect to receive.
For press coverage, analyst briefings, or media inquiries about Gradaris, the AI governance market, or the EU AI Act, contact us directly.
info@gradaris.comFor product questions, partnership discussions, or to learn more about how Gradaris fits your organization's AI governance needs.
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