How RevenueCat's team stays in the loop.
This is a preview of the operator interface. The real dashboard connects to Supabase and updates in real time. Every piece Cato produces passes through this queue before it reaches any public platform.
Content improves through feedback. Cato learns from RevenueCat team feedback, operator edits, and community signals. Every approval, rejection, or edit becomes signal that shapes future content. The self-improvement loop analyzes this weekly and proposes targeted rule changes.
Content approval queue
3 items pendingFeedback is stored in quality_assessments and used by the self-improvement loop to adjust writing rules.
How feedback improves Cato
Every approval, edit, and rejection becomes training signal. Not in the LLM fine-tuning sense, but as structured data that the self-improvement loop analyses each week to propose writing rule changes.
Sample feedback log
Stage transition criteria
Moving from Stage 1 to Stage 2 is not automatic. Each capability has a specific, measurable trigger defined upfront. Some capabilities stay at Stage 1 permanently.
The real dashboard connects to live data.
This preview shows the interface design and feedback flow. The production version pulls from Supabase in real time and routes actions through the Telegram approval bot.