Versioning - this v1.0 of this guide (published September 5, 2025)

The ambition with this guide is to provide an introduction to how Eyer’s GenAI Ops Playbook works to provide proactive actionable insights and alerts.

The high level principle is to use a LLM (Claude, ChatGPT, Gemini, Copilot, etc) to interact with the time series Anomaly Detection from the Eyer platform and with customers context to provide early, proactive actionable insights to reduce time to remediation.

This guide consist of five sections:

• A short text guide to serve as a high level introduction
• A 7 minute video recording of the process going a bit deeper and also showing the LLM, context and Grafana investigation dashboards
• A link to the architecture illustration / presentation to navigate on your own
• Key learnings from more than a decade working on this hard challenge
• Proof of Concept outline for those curious about testing this concept

<aside> 💡

About the Ivar Sagemo, the author of this guide:

• I have spent 25 years building B2B SaaS application with five-nines requirements for up time. I have held various CXO roles and been through IPO and trade sale to New York Stock Exchange and Thomson Reuters.
• I have learned the hard way. I have been yelled at by more angry customers than most out there.
• Eyer is the last venture to solve the pain I have lived with for decades: how to get proactive actionable insights to get ahead of customers experiencing problems.
• Feel free to connect with me on Linkedin or reach out at [email protected] or book a meeting with me here </aside>

1. Eyer GenAI Ops Playbook: Proactive Anomaly Detection with AI Integration

2. A 7 minute video recording of the concept

3. Link til architecture diagram / presentation

4. Practical Lessons Learned from 25+ Years in Observability

5. Eyer’s 3 week Proof Of Concept