PM Copilot
An MCP server that triangulates customer support tickets, feature requests, and AI support agent conversations to help PMs decide what to build next.
Real results: Analyzed 3,353 signals in one 30-day window: 1,678 support tickets, 276 feature requests, and 1,399 AI support agent conversations across 4 products.
The chats are signal a ticket-only analysis never sees.
Read the full story: I built an MCP server that changed how I prioritize products
What makes this different
- Signal triangulation. Matches support tickets against feature requests to find convergent themes, and gives convergent themes a 2x priority boost.
- The deflection blind spot. An AI support agent answers questions that never become tickets, so ticket-based prioritization undercounts every theme the bot handles. Chatbase conversations come in as a third signal class, with a per-theme
self_serve_failure_rate. - Composability. Pass churn or traffic data from other MCP servers into
generate_product_planviakpi_context, and the methodology adjusts priorities.
