Engineering teams adopted AI coding tools faster than any tooling shift before them. Within a year, the same developer may use Claude Code in the terminal, Cursor in the editor, an autonomous agent for migrations and half a dozen MCP servers that reach into databases and internal systems. Companies pay for all of it and can answer almost none of the basic questions: which tools are used, on which code, with what data, and whether the output survives.
MainLayer exists to answer those questions without turning into surveillance. We started from one architectural decision: the endpoint agent is the product, and it is built to see AI development sessions and nothing else. No keystrokes, no screens, no terminal history. Raw prompts and source code stay on the machine unless an organization deliberately turns content collection on for specific repositories.
The second decision is honesty about uncertainty. Every AI-contribution number carries the evidence tier it came from and its confidence. Every policy reports whether it was actually enforced, applied best-effort or only detected, because tools differ in what they expose. We would rather show a smaller, true number than a precise-looking guess.
Who we are
MainLayer is built by a small team of engineers who have run platform and security teams inside software companies. Our first customers are engineering organizations of 20 to 300 developers, initially in Turkey, with the product built for global use from the first commit.
Working with us
We are onboarding a limited number of design partners: teams that already pay for AI coding tools and want cross-provider visibility, governance and outcome metrics. Design partners get early-access pricing and a direct line to the people building the product. Write to [email protected].
We are not hiring publicly at the moment. If you have run developer platforms or security tooling and this problem keeps you up at night, the same address works.