An LLM pipeline for community feedback at scale
The problem.
A large, active community generates more feedback than any team can read by hand, and most of it never reaches the people who can act on it.
What I did.
I built an LLM-powered feedback pipeline that pulled from both structured bot submissions and passive chat monitoring, filtered for constructive signal, and generated daily, weekly, and monthly structured reports routed straight to the development team. During peak events like closed and open beta testing it covered hundreds of data points.
The constraint.
The hard part of feedback at scale is signal-to-noise. The pipeline had to separate the genuinely useful from the volume, reliably, every day, without a person reading each message.
The outcome.
The pipeline processed thousands of submissions a month and gave the development team a steady, structured read on what the community was actually saying, instead of an inbox nobody had time to open.