An AI model with a finger on the time series pulse
Release
Kim Martineau
AI for IT
AI can help sift through the noise to identify significant patterns in the increasingly large pools of data generated by enterprises. We’re reimagining IT operations, using AI to automate core processes, better detection and diagnosis, preformance monitoring, and explainable action recommendations.
Our work
Publications
A Perspective on LLM Data Generation with Few-shot Examples: from Intent to Kubernetes Manifest
- Antonino Angi
- Liubov Nedoshivina
- et al.
- 2025
- ACL 2025
Optimizing Telco Networks with Foundation Models: A Scalable Approach for Tabular Data
- Franck Le
- Keith Grueneberg
- et al.
- 2025
- ICDCS 2025
ITBench: Evaluating AI Agents across Diverse Real-World IT Automation Tasks
- Saurabh Jha
- Rohan Arora
- et al.
- 2025
- ICML 2025
Causal Latency Modelling for Cloud Microservices
- Christopher Lohse
- Diego Tsutsumi
- et al.
- 2025
- CLOUD 2025
Correlating Multimodal Data Through Representations for Enhanced IT Automation
- Seema Nagar
- Atri Mandal
- et al.
- 2025
- FSE 2025
LogAn: An LLM-Based Log Analytics Tool with Causal Inferencing
- Pranjal Gupta
- Karan Bhukar
- et al.
- 2025
- ICPE 2025
IBM Solution: IBM Cloud Pak for Watson AIOps
Our research has contributed to the development of IBM Cloud Pak for Watson AIOps, which deploys advanced, explainable AI across the entire IT operations toolchain to assess, diagnose, and resolve incidents for mission-critical workloads.
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