Your support tickets already know what to build next
Stop manually searching through 1,000+ daily tickets. ML-powered semantic search instantly clusters issues, extracts key details from long threads, and delivers actionable insights.
Predicted Volume Spike
VPN connection issues expected to increase 45% in 3 days
Built for scale
Purpose-built for networking, cybersecurity, and cloud infrastructure teams handling 1,000+ tickets daily
ML-Powered Semantic Search
Machine learning models understand context, not just keywords. Clusters tickets by actual root cause, even when described differently across hundreds of conversations.
Auto-Extract Key Points
Stop manually reading 50+ message threads. Automatically parse conversations and extract critical technical details, error codes, and resolution steps in seconds.
Universal Integration
Native integrations with Salesforce, Zendesk, Jira Service Management, ServiceNow. Fits into your existing workflow seamlessly.
Enterprise Scale
Handle 1,000+ tickets per day. AI identifies multiple root causes and tracks complex issue patterns across networking, security, and cloud infrastructure.
Predict Issues Before They Spike
ML models detect patterns 7 days before volume increases
"We predicted the iOS 18.2 VPN issue 5 days early"
Automatic Ticket Clustering
No manual tagging required. Groups similar issues by root cause, not just keywords
- AI-powered semantic analysis
- Auto-detects root causes
Sprint-Ready Engineering Handoffs
Direct export to Linear, Jira, GitHub Issues. Complete with root cause analysis and customer impact
Trusted by Industry Leaders
"KlusterCortex found a $2M ARR blocker we didn't know existed"
— VP Product, Enterprise Security Company
Built for Product and Engineering Teams
Extract intelligence from your support data with role-specific insights
Roadmap Prioritization
Prioritize features based on real customer pain, not just volume
Feature Request Validation
Validate requests with pipeline data and revenue impact
Churn Risk Signals
Early warning system for at-risk accounts based on support patterns