UST intelliRAN: AI-Powered Proactive RAN Failure Detection & Prevention

UST intelliRAN is an AI-powered RAN failure detection and prevention offering that helps communication service providers (CSPs) shift from reactive fault management to proactive network reliability management.

Traditional RAN operations often depend on alarms, tickets, and emergency response after degradation or outage conditions have already started. UST intelliRAN changes that model by analyzing fault management, performance management, alarm, event, log, and network operations data to identify early failure indicators and generate actionable predictions for operations teams.

Deployed on AWS, intelliRAN uses AWS Glue to orchestrate ETL into Amazon S3, AWS Lambda to support data ingestion and transformation, AWS Lake Formation for governed data access, Databricks on AWS for preprocessing, feature engineering, model R&D, and training, and Amazon SageMaker for experiment tracking, model registry, model sharing, and real-time inference. AWS Network Firewall helps secure inbound and outbound AWS traffic.

With intelliRAN, CSPs can detect probable failures earlier, prioritize preventive field interventions, reduce service-impacting incidents, and improve network performance across complex multi-vendor, multi-technology environments. The solution is designed to support proactive RAN operations, predictive maintenance, service assurance, network reliability, and operational efficiency at scale.

Key Features

• AI-powered RAN failure prediction: Applies machine learning models to predict RAN hardware failures and critical alarms before customer impact.
• Fault management and performance management analytics: Uses FM and PM data, alarms, events, logs, KPIs, alarm counters, and temporal patterns to identify early indicators of network degradation.
• AWS-enabled data and ML pipeline: Uses AWS Glue, Amazon S3, AWS Lambda, AWS Lake Formation, Databricks on AWS, and Amazon SageMaker to support governed ingestion, storage, feature engineering, model training, registry, and real-time inference.
• AIOps-driven correlation and alert noise reduction: Uses similarity correlation, hybrid correlation, intelligent event correlation, and ticket intelligence to reduce noise and focus teams on high-impact issues.
• SmartOps automation foundation: Supports integration, orchestration, runbook automation, ticket creation, notification, workflow automation, incident summaries, root cause analysis, recommended resolution, and similar-ticket insights.
• Discovery-to-production approach: Includes data landscape assessment, infrastructure evaluation, stakeholder alignment, integration planning, PoC execution, and production deployment readiness for CSP environments.

Key Benefits

• Predict failures before customer impact: Enables CSPs to identify probable RAN failures and critical alarms more than 24 hours in advance, supporting faster intervention and better service continuity.
• Reduce unplanned network downtime: Helps operations teams move from reactive outage response to preventive action, reducing service disruption and improving network availability.
• Lower emergency truck rolls: Gives field and network operations teams earlier visibility into likely hardware failures so they can prioritize planned interventions over costly emergency dispatches.
• Improve SLA performance and customer experience: Helps reduce the frequency and duration of customer-impacting incidents, protecting service quality, loyalty, and brand trust.
• Reduce RAN operations cost: Improves operational efficiency by reducing reactive repair cycles, manual triage effort, avoidable truck rolls, and fragmented operational workflows.
• Improve workforce productivity: Helps NOC, engineering, and field teams focus on the highest-priority risks instead of spending time sorting through alarm noise and reactive tickets.
• Accelerate time to value: Supports a structured 90-day implementation approach to validate data, tune models, and operationalize predictive insights.
• Enable scalable proactive operations: Combines AI/ML, AIOps, automation, AWS cloud services, and telecom expertise to modernize RAN operations across large-scale wireless networks.