How one helpdesk team tackled AI-powered support ticket deduplication

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A team building an internal helpdesk identified duplicate support tickets as their users' top pain point, with a single incident routinely generating multiple unrelated-looking reports across chat, email, and error trackers. Simple keyword-matching failed because related tickets like 'login is broken' and a TokenExpiredError share no common words, while embedding-based clustering risked confidently merging unrelated issues. The team also ruled out vector-clustering pipelines due to concerns about precision, particularly the costly risk of incorrectly merging distinct incidents. Their eventual solution was a deliberately minimal approach using a low-temperature AI triage model that compares each new ticket against a short list of open tickets, with optional code context for disambiguation. The team acknowledged the problem remains only partly solved and openly invited feedback from others who have tackled similar deduplication challenges.

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