How an emergency tech company scaled support operations with AI without adding headcount

The support organization struggled with fragmented workflows, inconsistent data, and limited visibility into operations.

Key challenges included:

• Unstructured and inconsistent ticket data
• Manual ticket routing and handling
• Underutilized knowledge base
• Lack of automated log analysis
• Limited reporting and unreliable metrics

This resulted in:

• Slow ticket resolution times
• High manual workload for support teams
• Poor visibility for leadership
• Difficulty scaling support operations as ticket volume increased

A full-scale AI-powered support operations transformation, redesigning workflows and introducing automation and intelligent agents across the support lifecycle.

The transformation included:

Data standardization & centralization

• Cleaned and structured ticket data across systems
• Created a unified data layer for reporting and analytics

Automated ticket routing

• Implemented AI-driven classification and routing
• Reduced manual triage and improved assignment accuracy

Knowledge base optimization

• Improved accessibility and usage of internal knowledge
• Enabled AI-assisted responses based on existing documentation

AI-powered log analysis

• Introduced automated log parsing and analysis for faster issue diagnosis

Customer-facing chatbot

• Deployed AI chatbot to handle common inquiries and reduce ticket volume

Reporting & analytics layer

• Built centralized reporting with reliable metrics for leadership

• Significant reduction in manual support workload
• Faster ticket routing and resolution times
• Improved data accuracy and visibility across support operations
• Scalable support function capable of handling increased ticket volume
• Better decision-making enabled by reliable, structured data and insights

Delivered through a phased transformation approach, covering data, workflows, automation, and AI enablement.

The implementation included:

• Cross-functional team (AI, engineering, automation, QA)
• Integration across support systems, logs, and knowledge bases
• Deployment of AI agents and automation scripts across workflows

The system was designed to be scalable, modular, and continuously improvable, enabling ongoing optimization of support operations.