NeuBird
NeuBird AI is a Production Ops Platform designed for ITOps, SRE, and DevOps teams running production cloud environments. It uses agentic AI to move operations from reactive incident response to proactive, autonomous production management.
Despite significant investment in monitoring and observability tools, teams still face alert noise, slow root cause analysis, and costly incidents. NeuBird AI solves this by continuously analyzing telemetry across cloud services, applications, and infrastructure to prevent issues, resolve incidents faster, and optimize operations.
Prevent incidents before they happen
NeuBird AI detects early signals of degradation, configuration drift, and anomaly patterns across metrics, logs, traces, and change events. Teams can identify and address issues 30 to 60 minutes before user impact while reducing alert noise by more than 78 percent.
Resolve incidents in minutes
When incidents occur, NeuBird AI automatically investigates across Azure Monitor, Amazon CloudWatch, logs, metrics, traces, and recent changes to identify root cause in minutes. AI driven triage, correlation, and runbook generation reduce mean time to resolution by up to 60 percent while minimizing the need for large war room responses or bridge calls.
Optimize cost, performance, and operations
NeuBird AI continuously analyzes cloud environments to uncover cost savings, performance issues, and gaps in observability. It identifies right sizing opportunities, missing telemetry, and repetitive operational tasks, helping teams reclaim more than 200 engineering hours per month.
Built for production cloud operations
NeuBird AI integrates with AWS services including CloudWatch, as well as Kubernetes and Azure Monitor, and tools like Datadog, Splunk, and PagerDuty.
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NetBrain
Since 2004, NetBrain has transformed network operations with its no-code automation platform, helping teams systematically shift left by turning complex processes into streamlined workflows. By unifying AI and automation, NetBrain delivers actionable hybrid network-wide observability, automates troubleshooting, and enables safe change management to boost efficiency, reduce MTTR, and mitigate risk, enabling IT organizations to proactively drive innovation.
Get network-wide and contextualized analysis across your multi-vendor, multi-cloud network
Visualize and document the entire hybrid network using dynamic network maps and end-to-end paths
Automate network discovery and ensure data accuracy for a single source of truth
Auto-discover and decode your network's golden configurations, discover day 1 issues, and automate configuration drift prevention
Automate pre- and post-validations for network changes with application performance context understanding
Automate collaborative troubleshooting from human to machine
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Infraon AIOps
A centralized approach driven by AI and machine learning is designed to handle vast quantities of IT-related data sourced from various platforms. This approach enhances the responsiveness of multiple teams to outages and performance issues while ensuring seamless interaction with IT service management technologies. By employing AIOps, organizations can effectively address daily IT operational challenges on a large scale, utilizing a range of advanced techniques such as machine learning, network science, combinatorial optimization, and additional computational methods. AIOps equips enterprises to manage an extensive array of IT management tasks, which includes intelligent alerting, correlating alerts, escalating alerts, automating remediation, investigating root causes, and optimizing capacity. Implementing a structured framework enables the proactive refinement of processes, resources, personnel, information, and communication channels. Continuous oversight and optimization of operations are essential, allowing for 24/7 management of IT functions. Additionally, establishing effective processes helps minimize the disruptive noise that often accompanies incident occurrences, ultimately leading to a more streamlined IT environment. This comprehensive strategy can significantly enhance overall operational efficiency and reliability.
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BigPanda
All data sources, including topology, monitoring, change, and observation tools, are aggregated. BigPanda's Open Box Machine Learning will combine the data into a limited number of actionable insights. This allows incidents to be detected as they occur, before they become outages. Automatically identifying the root cause of problems can speed up incident and outage resolution. BigPanda identifies both root cause changes and infrastructure-related root causes. Rapidly resolve outages and incidents. BigPanda automates the incident response process, including ticketing, notification, tickets, incident triage, and war room creation. Integrating BigPanda and enterprise runbook automation tools will accelerate remediation. Every company's lifeblood is its applications and cloud services. Everyone is affected when there is an outage. BigPanda consolidates AIOps market leadership with $190M in funding and a $1.2B valuation
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