Essert started from a simple observation: organizations were adopting AI systems faster than they could reliably answer basic questions about them — what data a system touches, whether its behavior still matches what was approved, whether a board or regulator could be shown proof rather than a policy document. Governance was being done by spreadsheet, if it was being done at all. We built Essert to make continuous, provable AI governance the default, not a project that starts the week before an audit.
We don't think governance should be a point-in-time checklist. AI systems change — through retraining, drift, new data, new use. Essert is built to watch continuously, because a governance program that only looks once a quarter is already behind.
A policy is not proof. We build Essert so that what it produces — scores, logs, reports — is something you can actually hand to a board, an auditor, or a regulator, not just something that describes your intentions.
Nobody governs one model in isolation. Organizations run portfolios — internal tools, vendor AI, copilots, systems nobody remembers approving. Essert is built to govern all of it, and to work alongside the tools already in place.