Canopus Networks Launches SEER Platform for Enhanced Network Observability

Feb 28, 2025
Canopus Networks has introduced the Canopus SEER platform, a real-time network observability tool powered by Agentic-AI, designed to provide enterprises with actionable insights from network data.

Canopus Networks has launched the Canopus SEER platform, a real-time network observability tool powered by Agentic-AI, announced in a press release. The platform aims to address the challenges faced by enterprises and carriers in managing vast amounts of monitoring data by providing actionable insights.

The Canopus SEER platform is built on 12 years of research and incorporates eight patents and over 30 academic papers. It offers a new approach to extracting real-time intelligence from networks at scale, supporting AI workflows for network and security operations teams. The platform's core innovations include high-performance cloud-native eBPF sensors, patented algorithms for contextualizing real-time data flows, and AI agents that automate repetitive tasks.

The platform is designed to overcome the limitations of traditional network telemetry, which is often fragmented and noisy, leading to delayed insights and increased costs. By providing deep visibility and enabling AI-driven decision-making, Canopus SEER aims to transform network observability from a reactive to a proactive process. The platform is now available globally and integrates with platforms like AWS, DataDog, and Google Cloud through open standards.

We hope you enjoyed this article

Consider subscribing to one of our newsletters like Enterprise AI Brief or Daily AI Brief.

Also, consider following us on social media:

Enterprise AI AI Brief AI Brief (X)
Free newsletter

Enterprise AI Brief

Weekly report on AI business applications, enterprise software releases, automation tools, and industry implementations.

AI Token Webinar

Whitepaper

Tensordyne Napier: What If One Rack Could Do the Work of Nine?

Tensordyne

This Tensordyne whitepaper presents Napier, an inference-focused AI processor and rack-scale system based on the company’s TDN Math logarithmic number system. It examines infrastructure requirements for large mixture-of-experts and agentic models, compares major inference architecture approaches, and details the TDN AIP processor, TDN72 pod, TDN Link fabric, and Napier Ultra configuration. The paper reports simulation-based performance, cost, and accuracy-validation results, including Tensordyne’s projected comparison of one Napier rack with a nine-rack Nvidia Rubin plus Groq deployment; the chip is reported as taped out and in fabrication.

Read more

You may also like

Free, six days a week

Daily AI Brief: the AI news that matters, in your inbox.