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57 Listings in IT Ops AI Available
What is Cleric? Cleric is a SRE AI agent offering an AI site reliability engineer that investigates alerts and finds root causes autonomously. Founded in 2023 and based in San Francisco, California, USA, Cleric helps SRE and platform teams automate SRE work and get results faster. Key capabilities of Cleric Autonomous alert investigation Root cause findings Observability integrations Learning from past incidents Root cause analysis Runbook automation How Cleric works Cleric takes alerts, logs and metrics as input and produces text and insights. It combines large language models with task-specific AI, with the vendor managing prompts, models and updates. It connects to tools such as Slack, PagerDuty, ServiceNow and Datadog, so the agent works inside existing workflows. Who uses Cleric? Cleric is built for SRE and platform teams. It suits teams that want autonomous alert investigation and root cause findings without adding headcount, while keeping people in control of review and final decisions. Cleric vs Resolve.ai Cleric is often compared with Resolve.ai. Cleric stands out for autonomous alert investigation and observability integrations. The right choice depends on your workflow, integrations and budget, so compare both on a real task.
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What is New Relic AI? New Relic AI is an observability assistant AI agent offering New Relic AI assistant and agents for querying telemetry and troubleshooting issues. Founded in 2008 and based in San Francisco, California, USA, New Relic AI helps engineering teams using New Relic automate observability assistant work and get results faster. Key capabilities of New Relic AI Natural-language NRQL Error analysis Agentic integrations Observability insights Root cause analysis Runbook automation How New Relic AI works New Relic AI takes text and telemetry as input and produces text and insights. It is powered by Multiple LLMs (managed) models, with the vendor managing prompts, models and updates. It connects to tools such as Slack, PagerDuty, ServiceNow and Datadog, so the agent works inside existing workflows. Who uses New Relic AI? New Relic AI is built for engineering teams using New Relic. It suits teams that want natural-language NRQL and error analysis without adding headcount, while keeping people in control of review and final decisions. New Relic AI vs Datadog Bits AI New Relic AI is often compared with Datadog Bits AI. New Relic AI stands out for natural-language NRQL and agentic integrations. The right choice depends on your workflow, integrations and budget, so compare both on a real task.
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Saaskart Market Grid™
Explore how leading IT Ops AI solutions compare based on customer satisfaction, market presence, adoption, and buyer feedback. The Market Grid helps you identify category leaders, high-performing solutions, and emerging products within the IT Ops AI ecosystem.
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Aisera
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What is Aisera? Aisera is an enterprise agentic AI platform that delivers autonomous AI agents and assistants across IT, HR, customer service, finance and other departments. It automates complex, cross-domain tasks and provides 24/7 support through intelligent agent orchestration. In 2025 Aisera was acquired by Automation Anywhere. Key capabilities Agentic automation, autonomous AI agents that execute complex actions across IT service desk, HR and customer service. Agent Composer & LLM Studio, build custom agents in natural language and tune models with organizational knowledge. Conversational AI, multi-channel self-service and Agent Assist to boost human agent productivity. Enterprise trust, the TRAPS framework (Trusted, Responsible, Auditable, Private, Secure) with ISO 27001, SOC 2 and HIPAA compliance. Who it's for IT operations and service-desk teams, HR, finance and customer service organizations across banking, healthcare, telecom, retail and the public sector, with reported ticket auto-resolution rates of 60-70%.
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What is Workativ? Workativ is an IT support AI agent offering a conversational AI platform that automates employee IT and HR support in Slack and Teams. Workativ helps IT and HR teams automate IT support work and get results faster. Key capabilities of Workativ AI IT support Password resets and access Knowledge answers Workflow automation Ticket triage and routing Automated resolutions How Workativ works Workativ takes text as input and produces text and actions. It combines large language models with task-specific AI, with the vendor managing prompts, models and updates. It connects to tools such as ConnectWise, Autotask, HaloPSA and ServiceNow, so the agent works inside existing workflows. Who uses Workativ? Workativ is built for IT and HR teams. It suits teams that want AI IT support and password resets and access without adding headcount, while keeping people in control of review and final decisions. Workativ vs Moveworks Workativ is often compared with Moveworks. Workativ stands out for AI IT support and knowledge answers. The right choice depends on your workflow, integrations and budget, so compare both on a real task.
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What is Selector AI? Selector AI is a network AIOps AI agent offering an AIOps platform that correlates network and infrastructure telemetry to find root causes. Founded in 2019 and based in Santa Clara, California, USA, Selector AI helps network operations teams automate network AIOps work and get results faster. Key capabilities of Selector AI Telemetry correlation Root cause analysis Natural-language queries Alert reduction Approval workflows How Selector AI works Selector AI takes metrics, logs and events as input and produces insights and alerts. It combines large language models with task-specific AI, with the vendor managing prompts, models and updates. It connects to tools such as Slack, Microsoft Teams, Jira and ServiceNow, so the agent works inside existing workflows. Who uses Selector AI? Selector AI is built for network operations teams. It suits teams that want telemetry correlation and root cause analysis without adding headcount, while keeping people in control of review and final decisions. Selector AI vs BigPanda Selector AI is often compared with BigPanda. Selector AI stands out for telemetry correlation and natural-language queries. The right choice depends on your workflow, integrations and budget, so compare both on a real task.
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What is Transposit? Transposit is an incident management AI agent offering AI-driven incident management with human-in-the-loop automation and runbooks. Founded in 2016 and based in San Francisco, California, USA, Transposit helps DevOps and SRE teams automate incident management work and get results faster. Key capabilities of Transposit Incident workflows Runbook automation AI incident summaries Slack-first response Root cause analysis How Transposit works Transposit takes alerts and text as input and produces actions and text. It combines large language models with task-specific AI, with the vendor managing prompts, models and updates. It connects to tools such as Slack, PagerDuty, ServiceNow and Datadog, so the agent works inside existing workflows. Who uses Transposit? Transposit is built for DevOps and SRE teams. It suits teams that want incident workflows and runbook automation without adding headcount, while keeping people in control of review and final decisions. Transposit vs PagerDuty Transposit is often compared with PagerDuty. Transposit stands out for incident workflows and AI incident summaries. The right choice depends on your workflow, integrations and budget, so compare both on a real task.
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What is Lasso Security? Lasso Security is a generative AI security AI agent offering a security platform that protects employee and application use of generative AI and agents from data leaks and attacks. Founded in 2023 and based in Tel Aviv, Israel, Lasso Security helps enterprise security teams automate generative AI security work and get results faster. Key capabilities of Lasso Security Shadow AI discovery Prompt injection defense Data leakage prevention Agent security Prompt and response guardrails How Lasso Security works Lasso Security takes text as input and produces alerts and policies. It combines large language models with task-specific AI, with the vendor managing prompts, models and updates. It connects to tools such as ChatGPT, Microsoft Copilot, Chrome extension and REST APIs, so the agent works inside existing workflows. Who uses Lasso Security? Lasso Security is built for enterprise security teams. It suits teams that want shadow AI discovery and prompt injection defense without adding headcount, while keeping people in control of review and final decisions. Lasso Security vs Prompt Security Lasso Security is often compared with Prompt Security. Lasso Security stands out for shadow AI discovery and data leakage prevention. The right choice depends on your workflow, integrations and budget, so compare both on a real task.
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What is SentinelOne Purple AI? SentinelOne Purple AI is an AI security analyst AI agent offering SentinelOne Purple AI for natural-language threat hunting, investigations and agentic triage. Founded in 2023 and based in Mountain View, California, USA, SentinelOne Purple AI helps SOC teams on SentinelOne automate AI security analyst work and get results faster. Key capabilities of SentinelOne Purple AI Natural-language hunting Investigation notebooks Auto-triage Singularity integration Natural-language threat hunting Guided response How SentinelOne Purple AI works SentinelOne Purple AI takes alerts and text as input and produces text and actions. It is powered by Multiple LLMs (managed) models, with the vendor managing prompts, models and updates. It connects to tools such as Microsoft Sentinel, Splunk, CrowdStrike Falcon and Okta, so the agent works inside existing workflows. Who uses SentinelOne Purple AI? SentinelOne Purple AI is built for SOC teams on SentinelOne. It suits teams that want natural-language hunting and investigation notebooks without adding headcount, while keeping people in control of review and final decisions. SentinelOne Purple AI vs CrowdStrike Charlotte AI SentinelOne Purple AI is often compared with CrowdStrike Charlotte AI. SentinelOne Purple AI stands out for natural-language hunting and auto-triage. The right choice depends on your workflow, integrations and budget, so compare both on a real task.
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What is Command Zero? Command Zero is a security investigation AI agent offering an AI-assisted investigation platform that guides analysts through expert-built cyber investigations. Command Zero helps SOC analysts and incident responders automate security investigation work and get results faster. Key capabilities of Command Zero Guided investigations Identity and cloud queries AI summaries Investigation playbooks Autonomous alert triage Investigation reports How Command Zero works Command Zero takes logs and text as input and produces investigations and reports. It combines large language models with task-specific AI, with the vendor managing prompts, models and updates. It connects to tools such as Splunk, Microsoft Sentinel, CrowdStrike and Okta, so the agent works inside existing workflows. Who uses Command Zero? Command Zero is built for SOC analysts and incident responders. It suits teams that want guided investigations and identity and cloud queries without adding headcount, while keeping people in control of review and final decisions. Command Zero vs Intezer Command Zero is often compared with Intezer. Command Zero stands out for guided investigations and AI summaries. The right choice depends on your workflow, integrations and budget, so compare both on a real task.
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What is Knostic? Knostic is a LLM access control AI agent offering need-to-know access controls for enterprise LLMs and copilots to prevent oversharing. Knostic helps enterprises deploying copilots automate LLM access control work and get results faster. Key capabilities of Knostic Oversharing detection Need-to-know policies Copilot readiness assessments Knowledge boundary testing Policy enforcement AI usage visibility How Knostic works Knostic takes enterprise data and prompts as input and produces insights and policies. It combines large language models with task-specific AI, with the vendor managing prompts, models and updates. It connects to tools such as Microsoft 365 Copilot, Salesforce, Okta and Slack, so the agent works inside existing workflows. Who uses Knostic? Knostic is built for enterprises deploying copilots. It suits teams that want oversharing detection and need-to-know policies without adding headcount, while keeping people in control of review and final decisions. Knostic vs Zenity Knostic is often compared with Zenity. Knostic stands out for oversharing detection and Copilot readiness assessments. The right choice depends on your workflow, integrations and budget, so compare both on a real task.
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What is Console? Console is an IT support automation AI agent offering an AI agent that resolves IT requests like access, onboarding and troubleshooting in Slack. Founded in 2023 and based in San Francisco, California, USA, Console helps IT teams automate IT support automation work and get results faster. Key capabilities of Console Access provisioning Onboarding automation Troubleshooting Policy-based approvals Root cause analysis Approval workflows How Console works Console takes text as input and produces text and actions. It combines large language models with task-specific AI, with the vendor managing prompts, models and updates. It connects to tools such as Slack, Microsoft Teams, Jira and ServiceNow, so the agent works inside existing workflows. Who uses Console? Console is built for IT teams. It suits teams that want access provisioning and onboarding automation without adding headcount, while keeping people in control of review and final decisions. Console vs Serval Console is often compared with Serval. Console stands out for access provisioning and troubleshooting. The right choice depends on your workflow, integrations and budget, so compare both on a real task.
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What is Qevlar AI? Qevlar AI is an autonomous SOC investigations AI agent offering an AI platform that autonomously investigates security alerts and produces transparent reports. Founded in 2023 and based in Paris, France, Qevlar AI helps SOC teams and MSSPs automate autonomous SOC investigations work and get results faster. Key capabilities of Qevlar AI Autonomous investigations Transparent reasoning SIEM and EDR integrations MSSP support Investigation reports Human-in-the-loop response How Qevlar AI works Qevlar AI takes alerts and logs as input and produces text and reports. It combines large language models with task-specific AI, with the vendor managing prompts, models and updates. It connects to tools such as Splunk, Microsoft Sentinel, CrowdStrike and Palo Alto Cortex XSOAR, so the agent works inside existing workflows. Who uses Qevlar AI? Qevlar AI is built for SOC teams and MSSPs. It suits teams that want autonomous investigations and transparent reasoning without adding headcount, while keeping people in control of review and final decisions. Qevlar AI vs Intezer Qevlar AI is often compared with Intezer. Qevlar AI stands out for autonomous investigations and SIEM and EDR integrations. The right choice depends on your workflow, integrations and budget, so compare both on a real task.
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IT Ops AI (AIOps) applies machine learning to IT operations, detecting and diagnosing incidents, reducing alert noise, automating remediation, and predicting issues, to keep systems reliable and teams focused. This guide explains what AIOps is, how it works, what matters, and how to choose one.
IT Ops AI (AIOps) applies machine learning to IT operations, detecting and diagnosing incidents, reducing alert noise, automating remediation, and predicting issues, to keep systems reliable and teams focused. This guide explains what AIOps is, how it works, what matters, and how to choose one.
AIOps (AI for IT operations) uses machine learning and analytics on IT telemetry, metrics, logs, traces, and events, to detect anomalies, correlate and reduce alerts, diagnose root causes, and automate or recommend remediation.
It spans AIOps platforms, observability tools with AI/ML features, and AI assistants for IT service management (ITSM) and incident response.
The category exists because modern systems generate overwhelming telemetry and alerts. Buyers weigh signal quality (noise reduction and accurate root cause), integration with their observability and ITSM stack, automation safety, and how much it actually reduces incidents and toil.
AIOps ingests telemetry from across the stack, learns normal behavior, detects anomalies, correlates related alerts into incidents, surfaces probable root causes, and triggers or recommends remediation, reducing noise and speeding resolution.
Platforms combine data ingestion from monitoring/observability and ITSM tools, anomaly detection and correlation models, root-cause analysis, and automation/runbooks.
IT and SRE teams connect data sources, tune detection and automation, and use AIOps to triage and resolve incidents faster, with humans approving or overseeing automated actions.
Learn normal behavior and detect issues across metrics, logs, and traces early.
Group related alerts into incidents to cut noise and alert fatigue.
Surface probable root causes to speed diagnosis and resolution.
Trigger runbooks and automated fixes, with approvals and guardrails.
Forecast capacity issues and potential failures before they occur.
Integrate with monitoring, observability, and ITSM tools for end-to-end workflow.
Correlation and noise reduction cut alert fatigue so teams focus on real issues.
Root-cause analysis and automation speed incident resolution.
Early anomaly detection and prediction prevent incidents before they escalate.
Automating routine remediation frees engineers from repetitive work.
Manage complex, high-telemetry systems that overwhelm manual ops.
| Type | Best for | Ideal size | Pros | Limitations |
|---|---|---|---|---|
| AIOps platforms | Correlation, RCA, automation | Mid-market to enterprise | End-to-end ops intelligence | Integration and tuning |
| Observability + AI | AI features in monitoring tools | Any | Unified with telemetry | Scope tied to that tool |
| Incident response AI | Triage and on-call assistance | Any | Faster incident handling | Needs good data |
| ITSM AI | Service desk and ticket automation | Any | Automates IT service work | Different focus than ops |
Technology: Technology IT and SRE teams use AIOps to reduce alert noise, detect and diagnose incidents faster, automate remediation, and predict issues, keeping systems reliable as complexity grows.
Healthcare: Healthcare IT and SRE teams use AIOps to reduce alert noise, detect and diagnose incidents faster, automate remediation, and predict issues, keeping systems reliable as complexity grows.
Financial Services: Financial Services IT and SRE teams use AIOps to reduce alert noise, detect and diagnose incidents faster, automate remediation, and predict issues, keeping systems reliable as complexity grows.
Retail & E-commerce: Retail & E-commerce IT and SRE teams use AIOps to reduce alert noise, detect and diagnose incidents faster, automate remediation, and predict issues, keeping systems reliable as complexity grows.
Education: Education IT and SRE teams use AIOps to reduce alert noise, detect and diagnose incidents faster, automate remediation, and predict issues, keeping systems reliable as complexity grows.
Professional Services: Professional Services IT and SRE teams use AIOps to reduce alert noise, detect and diagnose incidents faster, automate remediation, and predict issues, keeping systems reliable as complexity grows.
Manufacturing: Manufacturing IT and SRE teams use AIOps to reduce alert noise, detect and diagnose incidents faster, automate remediation, and predict issues, keeping systems reliable as complexity grows.
Media: Media IT and SRE teams use AIOps to reduce alert noise, detect and diagnose incidents faster, automate remediation, and predict issues, keeping systems reliable as complexity grows.
Test noise reduction and root-cause accuracy on your environment, this is the core value.
Confirm integration with your monitoring, observability, and ITSM tools.
Review guardrails, approvals, and rollback for automated remediation.
Verify it handles your telemetry volume and complexity.
Assess how much tuning and learning time before it delivers real noise reduction.
Understand data-volume, node, or seat pricing and how it scales.
Generative AI is adding conversational incident investigation and on-call copilots.
Autonomous remediation is expanding, with humans overseeing rather than executing.
Predictive and preventive ops are reducing incidents before they happen.
Buyers should prioritize signal quality, stack integration, automation safety, and measurable MTTR and noise reduction.
AIOps (AI for IT operations) applies machine learning and analytics to IT telemetry, metrics, logs, traces, and events, to detect anomalies, correlate and reduce alerts, diagnose root causes, and automate or recommend remediation. It spans AIOps platforms, observability tools with AI features, and AI assistants for incident response and IT service management, helping teams keep complex systems reliable.
It correlates related alerts from across the stack into single incidents and filters out noise, so instead of hundreds of disconnected alerts, teams see a few meaningful incidents. This cuts alert fatigue and helps engineers focus on real problems. Noise reduction and accurate correlation are among the most valuable AIOps capabilities, test them on your data.
Yes, AIOps can trigger automated runbooks and remediation for known issues, though safe deployment uses guardrails, approvals, and rollback so automation doesn't cause incidents. Many teams start with recommended actions and human approval, then expand autonomous remediation as confidence grows. Review automation safety controls carefully.
It can, by detecting anomalies early, speeding root-cause analysis to lower mean time to resolution (MTTR), and predicting issues before they escalate. Impact depends on signal quality and integration. Measure noise reduction, MTTR, and incident volume against a baseline to verify real improvement rather than relying on claims.
AIOps ingests data from your monitoring, observability, logging, and ITSM tools and pushes incidents and actions back into them. Integration breadth and depth vary and are essential to value, since AIOps sits on top of your telemetry. Confirm support for your specific observability and ITSM stack before adopting.
AIOps needs time to learn your environment's normal behavior before anomaly detection and correlation become accurate, and integration and tuning take effort. Time to value varies by tool and complexity. Ask vendors about typical ramp time and what tuning is required, and pilot to confirm it delivers noise reduction in your environment.
Telemetry can contain sensitive operational and sometimes personal data, and volumes are large. Confirm encryption, access controls, data residency, retention, and whether your data trains shared models. Review security and cost (often tied to data volume) carefully given the scale of telemetry involved.
Prioritize signal quality (noise reduction and root-cause accuracy) on your environment, integration with your monitoring and ITSM stack, automation safety controls, scalability to your telemetry, time to value, and pricing. Pilot in a real environment and measure noise reduction and MTTR before rolling out broadly.
