Operations | Monitoring | ITSM | DevOps | Cloud

We’re excited to announce that Lumigo Copilot is now integrated with Microsoft Teams, extending the power of our AI observability assistant beyond Slack and into your Teams-based workflows. Until now, Lumigo Copilot worked exclusively within Lumigo’s UI and Slack, where teams instantly ask questions about issues, receive AI-generated observability insights, and take action without leaving their collaboration space.

Picture this: Your phone rings in the middle of the night. It’s your engineering lead, calling to inform you of a significant outage affecting your customer-facing services. As your network operations team jumps into action, they’re greeted with chaos. Over 40 alerts flood their screens simultaneously. Your network, infrastructure monitoring, and application performance monitoring tools all fire independently, each with its own dashboard and presenting data in incompatible formats.

While AI offers powerful benefits for network operations, building an in-house AI solution presents major challenges, particularly around complex data engineering, staffing specialized roles, and maintaining models over time. The effort required to handle real-time telemetry, retrain models, and manage evolving environments is often too great for most IT teams.

In Large Language Model (LLM) applications, the quality of the training data is paramount in determining the final model performance. One of the most important steps in preparing datasets is cleaning and transforming raw data into similar and usable formats. However, this process can be tedious and time-consuming when done manually. Automating these data cleaning workflows is essential to improve efficiency and maintain consistency across multiple datasets.

Have you ever asked an AI model a question and received an outdated or completely off-base response? I’ve been there too. The problem is that most AI models rely solely on their pre-trained knowledge, which becomes obsolete over time. This is where RAG can help: RAG is a hybrid AI technique that combines the advantages of retrieval systems and generative models. It bridges the gap by bringing in real-time information from external knowledge sources to improve the generation quality.