The tools assumed a human wrote the code.
They were wrong.

The week NOWL started, three things happened in public: Aikido published an audit showing that 10.3% of Lovable's own showcase apps were leaking customer data. Bolt.host users discovered their OpenAI keys were shipped in client bundles. A vibe‑coded social network called Moltbook breached 1.5M API tokens three days after launch.

Anyone who had shipped a vibe‑coded app that month was an audit waiting to happen — the same Supabase rules, the same exposed keys, the same first‑answer code. The breach just hadn't arrived yet.

The existing security tools assumed a different threat model. They scan for the vulnerabilities a tired senior engineer writes at 2am, or the dependencies an enterprise procurement team would have approved. None of them knows what a language model produces when a founder types "build me an auth flow with Supabase" and accepts the first answer.

NOWL knows. The scanner was written from the assumption that an LLM wrote the input. The rules target the patterns Lovable, Bolt, Replit, v0, and Cursor produce in their default templates. The Hallucination Database catches the packages that don't exist on npm but the model insists on installing. The Fix with AI loop is constrained — same model family that broke the code is the one allowed to patch it, under a six-layer guardrail.

And pricing is determined by the value the product holds for those who use it, not by the price set by the sales team. Because we started out as a small team and then grew. We know the ropes.