Every team right now is treating AI governance like a documentation problem. Write a .cursorrules file. Add a CLAUDE.md. Hope the agent reads it. Hope it remembers after hour three. Hope someone updates it when the architecture changes. That is not governance. That is a README pretending to be a policy. I built something different. MarkdownLM treats your engineering rules as infrastructure, not documentation. Versioned, queryable, enforced before the code generates, with a receipt when violations get blocked.

What MarkdownLM does:

\- Your constraints live in a knowledge base the agent queries, not a prompt it might ignore

\- Context that matters gets surfaced before writing starts

\- Output gets checked against your rules before it reaches you, with a specific violation report and the smallest fix

\- Same enforcement across Cursor, Claude Code, GitHub Copilot, whatever your team uses next month

\- Your rules live in your terminal, managed like code, diffed, committed, shared

Usually documentation gets stale, prompts get buried and governance without enforcement is just suggestions. I focused on building the only layer that treats this as infrastructure, not a feature. Free BYOK. Your key, your stack, your agents. We run the governance. Every query is encrypted with AES-GCM-256 and your write-only key ensures your code and data never remain accessible on our systems.

I built the first infrastructure layer for AI coding rules (not documentation, not prompts)