Feedback as Fertiliser: from sh*t to quality with AI automation

Bugs get closed. Incidents get post-mortemed and forgotten. Support questions get answered once and lost in Slack threads. Every day, QA teams generate enormous amounts of feedback, and then flush most of it away.But in nature, nothing is wasted. Waste becomes compost. Compost becomes growth.

This talk shows how to build that same cycle into your QA practice. Using AI, retrieval-augmented generation (RAG), and low-code automation tools like n8n, you can turn the messy by-products of testing (duplicate bugs, half-answered questions, recurring incidents) into structured knowledge that actively improves quality over time.

You’ll see how a living knowledge base can shift QA from reactive firefighting to proactive prevention: surfacing recurring risk patterns, flagging duplicate reports before they reach engineers, and pointing teams toward the areas where testing effort matters most. We’ll walk through the architecture, the trade-offs, what worked, what didn’t, and the surprisingly small stack it takes to get started.

This isn’t another “AI will transform testing” talk. It’s a practical look at how to stop treating QA artifacts as disposable and start treating them as raw material for a system that gets smarter with every release. If you’ve ever felt like your team keeps solving the same problems in slightly different shapes, this talk is for you.