IntelliResQ: AI-Driven Network Outage Detection & Resolution Platform

UST IntelliResQ is an agentic AI network outage detection and resolution platform built on AWS for communications service providers. The solution helps CSPs move from manual, siloed outage response to intelligent, multi-agent operations that detect disruptions, diagnose root causes, assess customer impact, orchestrate remediation, and coordinate field dispatch in real time.

Network outages often take hours to resolve because operations teams must manually sift through alarms, correlate telemetry across multiple network domains, prioritize incidents, coordinate engineering response, notify customer care teams, and dispatch field technicians when physical repair is required. IntelliResQ brings these workflows together through an autonomous multi-agent framework that continuously analyzes OSS/EMS data, fault management, performance management, topology, care reporting, and operational context to accelerate outage response from detection through restoration.

Built using UST’s AI-powered Cloud Center of Excellence approach on AWS, IntelliResQ combines agentic AI, telecom domain expertise, cloud-native architecture, and standards-aligned delivery practices. The framework is designed to support network operations, AIOps, autonomous network optimization, customer impact analysis, field workforce coordination, and continuous learning across complex telecom environments.

Key Features

• Multi-agent outage resolution framework: Coordinates specialized agents for outage detection, customer care, root cause analysis, network remediation, field operations, and continuous learning.
• AI-driven outage detection: Ingests OSS/EMS telemetry and network snapshots to identify off-air sites, classify outage scope, and detect anomalies across telecom network infrastructure.
• Automated root cause analysis: Correlates RAN, power, transport, topology, upstream dependencies, alarms, and performance data to identify probable root causes and rank remediation priorities.
• Customer impact assessment: Evaluates affected geographies, subscriber segments, traffic impact, service degradation, and care reporting to help teams understand business and customer impact faster.
• Automated remediation orchestration: Supports policy-driven corrective actions, network optimization workflows, and remediation recommendations with guardrails for safe execution.
• Field operations coordination: Accelerates technician dispatch and physical repair workflows with data-enriched work orders, outage context, probable root cause, and site impact details.
• Event-driven AWS architecture: Uses Amazon Bedrock, Amazon Bedrock AgentCore, AWS Lambda, Amazon EventBridge, Amazon S3, Amazon DynamoDB, and Amazon QuickSight to support orchestration, processing, storage, analytics, and dashboards.
• Standards-aligned AI CCoE delivery: Uses codified governance, security, telecom compliance, architecture standards, and interface contracts to align development with CCoE, 3GPP, and TM Forum ODA patterns.
• Observability and executive dashboards: Enables visibility into outage trends, MTTR, automation rates, technician workload, service impact, and network resilience metrics.

Key Benefits

• Reduced network downtime: Accelerates outage detection, diagnosis, remediation, and field response so CSPs can restore service faster across complex telecom environments.
• Shorter mean time to resolution: Replaces manual alarm review, cross-domain correlation, and fragmented handoffs with coordinated multi-agent workflows.
• Lower operational cost: Reduces manual troubleshooting, unnecessary escalations, avoidable truck rolls, and overtime associated with prolonged outage response.
• Improved customer experience: Gives care teams earlier visibility into outage scope, affected geographies, subscriber impact, and service degradation for more proactive communication.
• Reduced SLA and revenue risk: Helps operations teams prioritize high-impact incidents faster, restore affected services sooner, and reduce exposure to SLA penalties.
• Greater network resilience: Uses incident outcomes, remediation actions, and operational feedback to improve future detection, prioritization, response accuracy, and automation.
• Smarter workforce utilization: Gives field teams richer outage context before dispatch, helping reduce unnecessary site visits and focus technical resources where they are needed most.