The inspiration came from watching AI adoption outpace governance inside organizations. As teams started integrating LLMs into their workflows, a critical gap emerged: there were plenty of tools to *use* AI, but almost nothing to *govern* it — no visibility into how models were being used, no policy enforcement, no cost control, no audit trails. The problem I was trying to solve is fundamentally organizational: when AI is no longer a novelty but an operational dependency, you need the same kind of oversight infrastructure you'd apply to any critical system. Who's using which models? What data is being sent to external APIs? Are there compliance risks? Most solutions I found either required enterprise contracts or were so tightly coupled to a specific vendor's ecosystem that they created new lock-in instead of solving the real problem. That's what pushed me toward building RegulaI as an open-source project. Budget should not be a barrier to responsible AI adoption — especially for mid-sized organizations or public-sector teams that need governance tooling the most but can least afford proprietary platforms. As for how the process evolved: I started narrowly focused on usage monitoring, but quickly realized that logging without policy enforcement is just overhead. The scope grew to cover the full governance lifecycle — from access control and rate limiting to audit logs and model routing rules. Each iteration was driven by the question: *what does a solo team or a growing company actually need to deploy AI responsibly, without a six-figure compliance budget?*

Regulai

Security layer between your users and AI providers

Security layer between your users and AI providers

Regulai

Previous Regulai Launches

Previous Regulai Launches