Managed IT service providers often handle high volumes of inbound service requests related to infrastructure management, cloud environments, cybersecurity, and technical support. These requests frequently involve time-sensitive issues that must be prioritized according to service-level agreements (SLAs). The objective of this project was to deploy AI-powered voice and web agents capable of managing inbound interactions, capturing incident details, and routing requests efficiently while maintaining consistent customer support.
Inbound calls and website conversations were handled manually, which limited scalability and slowed response times. With support requests arriving through multiple channels, the organization needed a system that could automate intake, classify incidents accurately, and ensure that critical issues were routed to the appropriate support teams.
Backend
Node.js, Python FastAPI
Integration
Twilio Voice API, Zendesk
Virstack implemented AI-powered voice and web agents designed to automate support intake and streamline incident management.
The voice agent handles inbound calls, performs intelligent routing, and filters spam calls while capturing incident details.
Incoming requests are categorized based on severity levels (P1–P4), enabling automated ticket creation and prioritization.
A website-based AI agent captures visitor inquiries, responds to common questions, and generates automated summaries for support teams.
A centralized dashboard provides real-time visibility into call activity, support ticket creation, SLA status, and system usage.
The AI automation system improved the efficiency and scalability of inbound support operations.
Faster intake and routing of inbound technical inquiries
Reduced manual workload for support teams handling calls and ticket creation
Real-time visibility into incident severity levels and SLA performance
Consistent customer support interactions across voice and web channels
Scalable support infrastructure capable of handling higher inquiry volumes