AI-Augmented Network Analysis

Positioned the platform for next-generation analytics through AI-assisted operations and anomaly detection.

Situation

Operators faced increasing data volumes and complexity, with limited ability to efficiently extract actionable insights from large-scale datasets.

Solution

Initiated development of AI-assisted capabilities, including GPU-accelerated analytics, agent-based operator support, anomaly detection, and integration with existing high-performance systems.

OUTCOMES

1 AI roadmap

network analysis operations

Improved analysis

through anomaly-led workflows

30% less

target-state operator effort

Challenges

Data

•Growing data volumes
•Rising complexity

Insight

•Limited actionable extraction
•Slow operator analysis

Scale

•Large dataset demands

Solutions

GPU Analytics Prototype

Prototyped GPU-accelerated analytics workflows.

Explored GPU-accelerated workflows for large-scale analysis
Improved readiness for more compute-intensive analytics tasks
Established a performance-oriented path for future AI features

Operator Assistance Agents

Explored agent-based systems for operator assistance.

Investigated agent-based support for operator workflows
Targeted faster interpretation of complex network data
Reduced dependence on fully manual analysis patterns

Anomaly Detection Research

Investigated anomaly detection across large datasets.

Evaluated anomaly detection approaches for large datasets
Improved the platform's future ability to surface unusual behavior

Platform Integration Design

Designed integration points with existing high-performance systems.

Planned integration with existing high-performance platform components
Reduced future adoption friction for AI-assisted capabilities
Preserved compatibility with established processing systems