PathSolutions has announced the launch of TotalView AI, a new capability within its TotalView platform that provides AI-driven troubleshooting for NetOps teams using network data analyzed on-premises.

As enterprise networks become more distributed and complex, NetOps teams face increasing pressure to resolve issues quickly, often without the benefit of deep domain expertise. At the same time, many AI-driven monitoring solutions rely on sending limited datasets to the cloud, introducing latency, increasing cost, and restricting visibility.

TotalView AI takes a fundamentally different approach.

By operating entirely on-premises, TotalView AI analyzes a broader and richer set of network data—without the constraints of cloud ingestion limits or transport delays. This enables faster, more accurate insights while ensuring data never leaves the customer’s environment.

“AI is only as effective as the data behind it,” said Tim Titus, CTO at PathSolutions. “With TotalView AI, we’re not sampling or filtering data to fit cloud pipelines. We’re analyzing the complete dataset locally, which allows us to deliver precise, real-time root-cause analysis.”

The on-prem advantage: More data, faster answers, greater security

TotalView AI is built on a core principle: better data leads to better outcomes. By keeping data collection and analysis on-premises, organizations gain several critical advantages:

• Complete data visibility: Analyze full-resolution telemetry without downsampling, filtering, or cloud-imposed limits.
• Real-time analysis with low latency: Eliminate delays associated with shipping data to the cloud, enabling immediate detection and response.
• No data movement constraints: Avoid bandwidth costs and architectural trade-offs required to export large volumes of network data.
• Air-gapped deployment support: Operate fully in high-security and regulated environments where external connectivity is restricted or prohibited.
• Data sovereignty and control: Keep sensitive network and operational data within organizational boundaries.

Key capabilities of TotalView AI:

• AI-powered root-cause analysis: Automatically correlates events across the network to identify the true source of performance issues.
• Correlation across complete datasets: Leverages comprehensive telemetry, including SNMP, flow data, and device-level metrics, for deeper insight.
• Faster mean time to resolution (MTTR): Reduces troubleshooting time from hours to minutes by guiding engineers directly to the problem.
• Empowering NetOps teams: Enables less-experienced engineers to diagnose complex issues with confidence.

Data completeness and correlation

While many vendors are introducing AI features, most rely on partial or sampled data, limiting their effectiveness. TotalView AI is designed to work with a complete, high-fidelity view of the network, ensuring more accurate conclusions and fewer false positives.

“The challenge in network operations isn’t just too much data—it’s incomplete data and lack of correlation,” added Tim Titus. “TotalView AI addresses both by analyzing everything, in real time, right where the data lives.”