AI & Trust
AI suggestions
with receipts.
Every action shows confidence scores, evidence sources, and reasoning. Priority, tone, queue status, category—all with full transparency. KB matches, Jira links, and code context when connected. Built into the product with no per-agent fees.
Incoming ticket
Production API returning 500 errors
Our checkout is completely broken. We're losing orders every minute. This needs to be fixed immediately...
AI suggestions
Priority
P1 - Critical
Tone
Frustrated
Queue
Waiting on us
Category
Bug
Capabilities
Practical AI that works out of the box
Purpose-built for support operations. AI capabilities included in every plan, not sold as expensive add-ons.
Automate triage
Tone analysis detects frustration and urgency. Priority is assigned automatically. Category (bug, feature, how-to) is set. Queue status routes tickets to the right team.
“Queue status is inferred from the entire thread, not just the last message.”
Generate replies
Draft replies grounded in your knowledge base, similar resolved tickets, and linked Jira context. Ready to send or refine as needed.
“Drafts cite which KB article or past ticket informed the suggestion.”
Connect context
Link Jira issues from tickets. Index GitHub repositories for code context. Surface similar tickets and email history. Everything stays within your tenant boundary.
“GitHub code search only runs if you connect repositories.”
Improve continuously
Confidence analytics show where AI performs well and where it needs attention. Knowledge gap detection surfaces topics your KB doesn't cover. The system learns from outcomes.
“Admins see aggregate accuracy metrics, not individual conversations.”
Reliability Guarantees
Enterprise-grade AI you can rely on
Tenant data stays isolated — no cross-customer access
Confidence scores visible on every action
Your data never trains our models
Only accesses sources you explicitly connect
Full audit trail of all AI actions
Configurable oversight for sensitive operations
Admins control what sources are connected. Nothing is indexed without explicit configuration.
Transparency
See the reasoning unfold
Every analysis shows its work. When AI is uncertain, it says so explicitly.
Ticket excerpt
We've been waiting for a response for 3 days now. Our enterprise contract specifically includes 24-hour SLA and this is clearly being ignored. The export feature is still broken and we can't generate reports for our board meeting tomorrow.
Frustrated
Repeated emphasis on delays
High
SLA violation + board meeting deadline
P1 - Critical
Enterprise customer + business impact
Waiting on us
Customer waiting for response
When confidence drops below 60%, the AI withholds its suggestion.
You see exactly where it's confident and where it needs human judgment.
Data boundaries
Your tenant, your data
AI only sees sources within your tenant boundary. Nothing crosses to other customers. Nothing trains our models.
Your tenant boundary
Outcomes
Measurable impact from day one
AI handles the routine. Your team focuses on what matters.
Faster triage
Priority and category assigned automatically. Tickets routed to the right queue.
Faster resolution
AI drafts replies from context. Your team sends or refines as needed.
Less duplicate work
Similar tickets and Jira issues surfaced. Merge duplicates, link related.
Predictable costs
Flat pricing with AI included. No per-seat fees, no surprise bills.
Technical details
How it works in 30 seconds
- 1
Ticket arrives. Content is analyzed and matched against connected sources (KB, Jira, GitHub, email history).
- 2
AI generates suggestions. Priority, tone, category, queue status, and draft replies—each with confidence scores and evidence.
- 3
Agent decides. Accept, modify, or reject. Feedback improves future confidence calibration.