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The workflow begins with Cross-Domain Data Ingestion, where diverse event types from telecom networks undergo intelligent processing. This is a critical vendor agnostic capability of the platform to collect data from and integrate with myriad telco platforms including network devices, network management system, ticketing systems, OSS tools, ITSM, probes, DPI platforms in addition to other platforms generating network events. Ensemble ML models combine statistical and deep learning approaches for robust anomaly detection. Unsupervised learning algorithms cluster similar events to reduce noise and help operations teams focus on critical issues rather than alarm storms.

Building on Dynamic Topology Assembly, the platform addresses a critical challenge in modern networks: incomplete dependency documentation. Where explicit topological information is missing, advanced inference techniques and intelligent tag-overloading complete the picture. ML algorithms automatically generate and update tags based on discovered relationships and contextual information, ensuring the correlation engine maintains comprehensive understanding of network dependencies.

This is where Event Intelligence transforms from concept to capability. The platform links incidents from multiple domains and information sources, utilizing both time-based and network topology analysis to cluster interconnected events with precision. This sophisticated correlation transcends basic chronological grouping by recognizing the intricate interdependencies and cascading effects inherent in complex telecommunications infrastructure.

Leveraging Advanced Pattern Recognition capabilities, Large Language Models generate human-readable incident descriptions from technical event data, translating complex information into clear, actionable narratives. Root cause narrative generation provides natural language explanations of probable causes based on correlated events, helping operations teams understand not just what happened, but why it likely occurred.

The workflow culminates with Automated Response and Remediation, where AI-driven insights trigger self-healing actions. This leverages the rich API integration capabilities of the platform to trigger actions (such as resource deployment, order provisioning, etc.) with the option of human-in-the-loop when deemed necessary. The platform uses clustering-based optimization and ML-based forecasting to determine optimal resource allocation and predict time-to-saturation measures, ensuring both immediate problem resolution and strategic resource deployment.

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Predictive Intelligence: Advanced Platform for Proactive Operations

Once the Intelligent Correlation Workflow is operational and is grouping events into incidents, GreySkies unlocks its most sophisticated AI capabilities for predictive incident detection. This represents the evolution from reactive incident management to AI-driven

proactive service assurance, fundamentally changing how telecommunications operators approach network reliability.

The GreySkies platform models incidents as objects with many features. As the platform accumulates incident data, intelligent feature selection and extraction is applied to accumulated incident data. Feature extraction discovers latent patterns in the incident data, identifying subtle patterns that might not be obvious through traditional analysis. Relying on historical incident data, clustering models are trained to detect the leading indicators of incidents, enabling proactive incident recognition and leading to possible prevention.

It is important to note that implementation considerations for AI models require comprehensive understanding of the technical requirements. Model training and optimization demand comprehensive training on telecom-specific datasets, with continuous model retraining and hyperparameter optimization.

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Real-World Implementation Excellence

GreySkies excels across diverse telecommunications environments, demonstrating its versatility and effectiveness to operate on the diverse and complex environments of an converged network operator.

In Fixed Access Services Topology environments, the platform manages complex service chains spanning OLT Sites, PE Routers, IGW Routers, DHCP servers, AAA systems, DNS servers, Wire Filters, and CDN infrastructure. The platform tracks service-level KPIs such as aggregated 95th percentile RTT and aggregated downlink volume, while simultaneously monitoring element-level KPIs including CPU utilization, link utilization, interface throughput, and memory utilization across all network components, as well as various element-level alarms and configuration management logs.

For 4G and 5G Mobile Data Networks spanning Edge-Backhaul-Aggregation architectures, the platform correlates performance across eNodeB, IP RAN elements, and Microwave elements. The platform tracks critical service metrics including subscriber RAN RTT, packet loss, and total volume, while monitoring performance indicators such as PRB utilization, hardware utilization, RTP jitter, and modulation penetration. This comprehensive monitoring ensures that mobile service quality issues are detected and resolved before they impact subscriber experience.

In Telco Cloud Environments with VNF-VM-Host architectures, the platform provides multi-layer visibility from service level metrics like Create Bearer Success Rate through VNF level indicators such as DRA TPS and Node Alarms, down to VM level metrics including CPU utilization and steal time, and host level indicators such as VM count and resource utilization. This layered approach ensures that issues in virtualized

environments are quickly traced to their root cause, whether in the application, virtualization, or hardware layer.

Implementation Results

GreySkies has been rolled out in production across a range of telecom operators covering the use cases above. Below are some of the tangible data-backed gains:

• 33 % average reduction in Mean Time to Repair (MTTR), measured across multiple fault-management use cases.

• 71 % faster root-cause identification, with event-correlation engine.

• 27 % better network resource utilization, achieved through machine-learning-driven resource allocation.

These real-world results demonstrate GreySkies’ ability to deliver measurable operational efficiencies in real-world networks. These results ultimately translate directly to positive business outcomes, including revenue loss avoidance, improved customer experience and operational cost efficiencies

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Conclusions

GreySkies AIOps replaces the legacy threshold-based monitoring at many operators today with an integrated layer of AI-driven network intelligence. Our out-of-the-box data collection and integration capabilities (with both legacy OSS platforms and modern ones),

real-time streaming analytics, batch processing, correlation engines, and automated responses turn operations from reactive troubleshooting into proactive optimization across fixed, mobile, and telco-cloud domains. An ecosystem of machine-learning anomaly detection, deep-learning analytics, and LLM-enhanced insights equips operators to spot issues early, diagnose them fast, and act automatically.

Deployed at scale, GreySkies lowers operating costs, boosts service quality, and adapts as the network grows learning from every event to deliver ever-better results. For network operators intent on modernizing assurance while safeguarding and enhancing customer experience, the platform offers a proven, future-proof path to measurable business impact and sustained competitive advantage.

Ready to revolutionize your network operations with GreySkies? Let us prove it - in your environment, on your data. See how the GreySkies platform delivers measurable results tailored to your priorities. Contact us today!