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Logz.io Introduces AI-Driven 'IQ Assistant,' Turning Observability Into a Conversation, Enabling a Wide Range of Users To Easily Obtain Immediate Insights

Logz.io announced the launch of Observability IQ, its branded AI strategy and roadmap, supported by the immediate introduction of its IQ Assistant. IQ Assistant is an AI-driven, chat-based capability that provides Open 360 users with immediate insights about emerging issues, along with related context, conclusions and response steps, helping human experts resolve issues faster and drive down mean time to remediation (MTTR).

IQ Assistant Leverages AI to Help Human Users Accelerate MTTR 

Logz.io IQ Assistant automatically generates context-aware questions based on users' interactions with the Open 360 platform and data to help accelerate and guide their observability practices. This targeted AI capability enables customers to simplify and accelerate their querying practices by leveraging the power of Logz.io's generative AI integrations, allowing for interaction in a more targeted, real-time manner.

IQ Assistant directly answers users' questions, engaging in conversations about their data and providing detailed textual responses, allowing users to dive deeper into their data and better understand the current state of their systems. In addition to searching out specific issues that users might suspect to exist, this type of natural language search is also a game changer in allowing teams to cast a wider net to unearth potential issues that might otherwise be overlooked.

IQ Assistant is informed by both proprietary Logz.io AI along with LLM, combining both the unique value of Open 360 with the widest breadth of available data on known errors, proven troubleshooting and response steps. Over time the system will become even more effective based on its ability to learn about the customer's environment and actions undertaken by human users that result in effective analysis and mitigation. 

"Engineers need to reduce mean time to remediation, yet they are buried in data," said Asaf Yigal, CTO and co-founder of Logz.io. "Applying AI in observability is going to help us finally deliver relief and simplify engineers' work so they can investigate, analyze and resolve events faster, thus reducing MTTR. Our new IQ Assistant is one example of how leveraging AI in observability delivers ease of use and practical value, and so many more applications of AI in observability are on the horizon."

IQ Assistant is the Newest Component of the Logz.io "Observability IQ" AI-based Layer of the Open 360 Platform

Logz.io is formalizing its AI strategy and roadmap under the Observability IQ brand, which will include numerous additional capabilities and announcements in 2024 and beyond. The AI-based capabilities in Observability IQ are purpose-built to help Open 360 users address their core challenges around ease of use, time to value, resource optimization and cost efficiency.

Today, Observability IQ elements in the Open 360 platform include:

  • Anomaly Detection for App 360: taps AI to detect services anomalies, providing real-time insights into the performance of selected services, operations, metrics and endpoints.
  • Alert Recommendations: uses supervised machine learning to model actions taken by users and then advises subsequent users when faced with similar issues.
  • Data Optimization Hub: delivers AI-based observability pipeline analytics, providing users with automated recommendations to ensure only the most critical data is ingested into the platform, an intelligent way to manage observability data volumes and costs.
  • Cognitive Insights: leverages generative AI to find what's important in log data, giving engineers the data they need, including links to related information and best practices, to quickly resolve issues in the production environment.
  • IQ Assistant: enables customers to accelerate and refine querying by automatically generating analysis cues and converting plain text to Lucene using generative AI integration.
Published Tuesday, May 21, 2024 2:21 PM by David Marshall
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