Intelligence that runs on your data. Not the internet's.
Response suggestions, pattern detection, and risk signals. Every AI output is attributed, auditable, and operator-controlled.
Suggestion acceptance at 5k tickets
Earlier pattern detection vs baseline
Suggestion attribution — no black box
What's included
- Response suggestions from your resolved ticket history
- Real-time confidence score and source attribution on every suggestion
- Issue pattern detection with velocity-based alerting
- Risk signals on escalation probability and SLA breach likelihood
- Suggestion acceptance tracking and model improvement over time
- No cross-workspace or cross-organisation data use
- Agent-controlled — all suggestions are editable, none are auto-sent
- AI output audit log
Suggestions from your history, not a generic model
The AI does not generate responses from training data. It surfaces resolution patterns from tickets your team has already closed. When a new ticket arrives, it finds the closest matches and renders the winning resolution as editable draft text. The source tickets are visible to the agent.
Pattern detection that fires before volume peaks
Issue clustering runs continuously on incoming ticket velocity. When the rate of similar tickets exceeds your baseline by a configurable threshold, the system alerts — typically 60–90 minutes before the issue would be visible in volume metrics alone.
The model gets better with every closed ticket
Suggestion acceptance, modification, and rejection are all training signals. The system learns that your edited version is preferred, not the original. At 5,000 closed tickets, suggestion acceptance typically exceeds 60%. At 500, it is around 34%.
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