MergeCoreAI
Merge lookalike tickets under one problem record.
MergeCoreAI detects tickets describing the same root issue and links them under a single problem record for one clean reporting axis.
- Clusters tickets opened from the same root cause using semantic similarity, time proximity, and service signals.
- Proposes a master record and auto-links the duplicates to it, creating a single reporting axis.
- Measures impact on one record so CSAT and service levels are never double-counted.
- Surfaces high-confidence merge candidates to L1 agents for one-click confirmation.
From signal to outcome
Semantic clustering
Each new ticket is compared in vector space against the nearest open records to find content matches.
Multi-signal scoring
Service, time window, keywords, and user segment are combined into a weighted similarity score.
Confirmed merge
Candidates above 85 percent are flagged in the L1 queue, where an agent confirms or separates them with one click.
Single reporting axis
Merged child tickets are auto-referenced to the master record so CSAT and service levels are tracked as one metric.
The situation
Thirteen minutes into a mail server outage, users from seven different branches each opened their own ticket. The wording varied widely, from 'mail won't open' to 'Outlook keeps freezing' and 'can't reach the server,' but every report described the same incident.
The outcome
MergeCoreAI detected six duplicate records at over 87 percent similarity and linked them all under INC-2847. The L1 team avoided 36 minutes of duplicate handling, the response queue collapsed into one item, and the resolution update reached every affected user through a single channel.
Common questions
No. It only auto-clusters and scores candidates; merges above the 85 percent threshold are flagged in the L1 queue, and an agent confirms or separates them with one click. Nothing is consolidated without human approval.
See MergeCoreAI in your workflow.
Book a demo and we'll show MergeCoreAI working inside the platform — on your real tickets and data.