OpenSea
Turning user issues into shipped fixes automatically
Signal vs. Noise
OpenSea's team talks to users across Twitter/X, Discord, and support. With thousands of messages pouring in, it was hard to separate signal from noise and turn messy threads into clear, actionable work.
The team struggled to spot issues affecting many users vs. just one and route the right context to the right owners in Linear.
It's really hard to identify issues affecting lots of users and triage those effectively.
Automated Feedback Triage
Cotera's agent sits on top of OpenSea's feedback channels and:
Ingests & normalizes feedback
Processes Discord, X/Twitter, and support conversations into structured data
Clusters related reports
Groups similar issues to show impact at a glance and identify widespread problems
Summarizes and structures
Transforms long threads into crisp, structured context that humans and AIs can understand
Auto-routes to Linear
Categorizes issues (bug vs. feature, severity) and routes to the right team with proper context
Proposes fixes automatically
In some cases, kicks off AI coding workflows to create draft pull requests
Faster Time-to-Fix
AI-powered triage
Teams review a short, prioritized list instead of sifting through thousands of messages
Faster time-to-fix
Issues arrive with clean context or draft PRs instead of dying in the backlog
Better prioritization
Clustering makes it obvious what affects one user vs. thousands
Higher confidence
Team knows they're working on the right things with proper context
With Cotera, we whittle down what humans need to review, route it to the right owners, and often have a PR fixing the user issue instead of a ticket aging in the backlog.
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