Telemetry Now

Kentik’s Mav Turner joins host Phil Gervasi to go beyond chatbot hype and dig into real AI reasoning for network operations. They discuss how Kentik AI Advisor uses network intelligence, hybrid RAG, and tool-calling to troubleshoot issues, optimize cost, and democratize access to network expertise. Along the way, they cover architecture, data governance, model evaluation, and why AI has to be built into an observability platform itself, not bolted on.

Host Philip Gervasi talks with Kentik pre-sales engineer Sean McGinley about what it really means to work in pre-sales. They unpack the various titles associated with those roles (e.g., solutions engineer, solutions architect) and discuss how pre-sales bridges the gap between technology and business. Learn how these roles balance hands-on engineering with customer relationships. Along the way, they share personal stories, lessons learned from the field, and why pre-sales can be one of the most rewarding career paths in tech.

Host Philip Gervasi and Kentik Field CTO Justin Ryburn preview the upcoming AutoCon 4 conference, one of the premier events for the networking industry. They discuss the growth of the Network Automation Forum, the evolution of the AutoCon community, and the event’s focus on real-world network automation, observability, and AIOps. Justin previews Kentik’s hands-on AutoCon 4 session, “Building Smarter Observability with Network Intelligence.”

Kentik CPO Mav Turner joins host Philip Gervasi to cut through the AI hype in NetOps. They discuss where ML and LLMs actually help—anomaly detection, root cause analysis, and agent-driven runbooks—and where deterministic methods still win. Join us for real talk on data pipelines, telemetry quality, model evaluation, human-in-the-loop guardrails, and the build-vs-buy trade-offs that transform network noise into informed decisions.

Doug Madory joins us to unpack the recent Red Sea submarine cable cuts and how Kentik’s Cloud Latency Map revealed the global impact in real-time, offering critical insight into cloud performance, interconnectivity, and internet resilience.

Lauren Basile joins us to show how traffic-aware cost intelligence turns spreadsheet guesswork into one-click, per-slice cost estimates across customers, ASNs, and CDNs. Learn about the SNMP plus contracts foundation, the flow-data leap, and how NetOps teams use cost-per-Mbps and path insights to optimize spend, pricing, and margins.

AI training isn’t just about GPUs, it’s about the network that ties them together. Host Phil Gervasi sits down with Vijay Vusirikala to unpack why job completion time is the true metric of success, how optical interconnects shape AI data center performance, and why power efficiency and observability are becoming mission-critical.

In this episode of Telemetry Now, Phil and guest David Cliffe explore “neocloud”—what it is, why it’s growing fast, and how it’s reshaping AI infrastructure. From GPUs and high-speed interconnects to orchestration layers and energy constraints, we discuss what makes neocloud different from traditional cloud, and what it means for data center operators, service providers, and AI architects.

In this Telemetry News Now episode, Phil Gervasi and Justin Ryburn tackle Intel’s plan to spin off its Network & Edge Group (NEX) and what it means for high‑performance Ethernet NICs, examine Cisco’s new partnership with Hugging Face to scan every open‑source AI model for malware, and break down Broadcom’s Jericho4 fabric router—bringing 3.2 Tb/s “hyper‑ports” to distributed AI clusters. The hosts also discuss the real‑world state of SD‑WAN adoption, Palo Alto Networks’ $25 B CyberArk acquisition, Cisco’s quantum‑networking research, and upcoming industry events. Plus: a personal tale of basement‑automation triumph to kick things off.

Phil Gervasi sits down with Kentik Product Marketing Manager Eric Hian-Cheong to discuss why data enrichment is the "secret sauce" that turns raw flow logs, metrics, and cloud telemetry into true network intelligence. They explore how tagging telemetry with human-readable context—such as customer names, app IDs, Kubernetes labels, and more—shrinks mean-time-to-insight, empowers cross-team troubleshooting, and lays the groundwork for AI-driven operations.