The Problem
In large businesses, the hardest part isn't building the product — it's understanding what's broken. Customer feedback pours in from everywhere: support tickets, app reviews, social media. Most of it goes unread, or at best, manually triaged.
The result? Real product glitches get buried under noise, and teams react to loud users rather than systemic issues.
What Glytch does
Glytch automatically ingests user feedback and uses AI to segment it into categories — distinguishing actual technical bugs from general dissatisfaction. It then measures both frequency (how often a problem appears) and impact (how severely it affects users), surfacing the issues that actually need fixing.
How it works
- Feedback is collected from multiple channels and normalized
- An AI classifier segments feedback by type (bug, feature request, complaint, etc.)
- Problems are scored by frequency × impact to create a priority matrix
- Product managers see a ranked dashboard of what to fix next
Tech Stack
- Java + NodeJS (backend services)
- Firebase (real-time data)
- HTML5 + CSS3 (frontend)
- AI/ML (feedback classification)
Recognition
1st Place — NanoGiants GmbH International Hackathon, Germany (September 2021)
Winner of the "AI Value" category, competing against teams across Europe.