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BMC AMI Ops

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What is Unravel?

Unravel Data is an AI-native data observability actionability™ platform that helps enterprises manage performance, reliability, and cost across their entire data ecosystem. It introduces intelligent, automated agents that collaborate with data teams to identify issues, guide decisions, and execute optimizations. Unlike traditional monitoring tools, Unravel focuses on actionability, enabling teams to detect, fix, and prevent data problems at scale. The platform combines data observability with FinOps to help organizations control cloud spending while maintaining high performance. Specialized agents for FinOps, DataOps, and Data Engineering automate cost governance, troubleshooting, and performance optimization. Unravel can take direct action to reduce toil, integrate with existing systems to automate workflows, or recommend actions teams can execute themselves. It provides deep visibility into pipelines, queries, applications, and infrastructure. Native integrations with Databricks, Snowflake, and Google Cloud BigQuery deliver platform-specific insights and optimizations. With real-time monitoring, root cause analysis, and automated remediation, Unravel dramatically reduces firefighting time. Enterprises use Unravel to improve platform resiliency, availability, and efficiency. Its AI-driven approach ensures continuous optimization as data environments evolve. Unravel enables data teams to move faster, spend smarter, and operate with confidence at enterprise scale.

What is BMC AMI Ops?

BMC AMI Ops is an AIOps-powered mainframe operations platform that helps enterprises improve observability, resilience, performance, automation, and cost control. The solution is designed for teams managing critical mainframe environments where blind spots, alert noise, manual triage, and performance issues can create operational risk. BMC AMI Ops provides a single integrated view across multiple mainframe systems and subsystems, giving teams clearer visibility into z/OS, z/OS UNIX, CICS, Db2, IMS, IBM MQ, Java workloads, networks, storage, batch processing, and capacity drivers. Its AI-driven anomaly detection helps identify unusual behavior and potential issues before they become service disruptions. Self-learning AI and machine learning models continuously adapt to system behavior, improving the accuracy of problem detection over time. Embedded GenAI capabilities translate findings into context, business impact, and guided remediation so operators can move from alert to action faster. The platform also supports OpenTelemetry-compliant streaming, helping organizations extend mainframe observability into broader enterprise monitoring and analytics platforms. BMC AMI Ops helps consolidate alerts, reduce operational noise, automate repetitive tasks, and improve mean time to detect and resolve problems. Its automation and optimization capabilities can reduce human error, improve resource utilization, lower CPU and MIPS usage, and support more efficient mainframe operations. The broader capability set includes monitoring, automation, network visibility, CICS control, Db2 optimization, IMS insights, Java environment monitoring, MQ management, storage oversight, batch optimization, cost analytics, console management, and real-time operational data streaming.