AI duplicate detection is the automatic identification of feature requests that ask for the same thing using different words. One user says "I want dark mode," another says "the white background hurts my eyes at night." Same request, different phrasing. Keyword search won't catch it.
It works by using embeddings to compare the meaning of requests, not just the words. When a new submission comes in, the system checks it against your backlog and flags potential matches above a similarity threshold.
Why this matters
Duplicates distort your vote counts. Ten people asking for the same thing across five posts looks like a low-priority item. Merge them and it becomes your most-requested feature. That changes your roadmap.
It also saves real time. Manual deduplication on a high-volume board isn't a task, it's a job. AI handles the first pass, and a human reviews flagged matches before anything gets merged.
The threshold tradeoff
A tight threshold flags only very close matches, which is safe but misses a lot. A loose threshold catches more, but you'll review false positives. Most teams tune based on false positive rate over time.
The goal isn't perfect automation. It's removing 80% of the manual work so your team can focus on the judgment calls.