OpsWorker is an AI-powered Site Reliability Engineering (SRE) platform that automatically investigates production incidents by correlating telemetry, logs, infrastructure state, and recent code changes. The platform delivers root-cause analyses and remediation steps—including copy-paste kubectl commands—directly to Slack within two minutes of an alert firing. It also builds a living model of a company's production system to identify inefficiencies and propose fixes as pull requests.
Funding
Funding not disclosed
Founders
Product
Problem
Engineering teams running complex cloud-native environments, particularly Kubernetes-based systems, face significant challenges when investigating production incidents. The knowledge required to troubleshoot issues is deep, contextual, and hard to transfer, and even with standardized platforms, runbooks, and observability standards, engineers still struggle to understand why workloads behave differently or why alerts fire repeatedly. This investigation burden is expensive, time-consuming, and often requires finding the right engineer to manually figure out what went wrong under pressure.
Solution
OpsWorker provides an AI SRE Production Intelligence platform that automatically investigates incidents as soon as alerts fire from tools like Prometheus, Datadog, or CloudWatch. The platform examines pods, services, deployments, logs, events, configurations, and resource relationships in parallel, evaluating multiple hypotheses and correlating infrastructure changes with recent deployments. It delivers root-cause analysis with specific remediation steps—including copy-paste kubectl commands—directly to Slack in under two minutes, without requiring engineers to open a terminal or learn new dashboards. Beyond incident response, OpsWorker builds a continuously evolving model of the production system, learning how services interact and where risks accumulate, enabling it to identify inefficiencies, scaling issues, and resilience gaps while proposing fixes as pull requests.
Target Audience
Primary customers are engineering and platform teams running Kubernetes in production, including DevOps, SRE, and infrastructure teams at companies of all sizes—from fast-moving product startups to large enterprises—who need to reduce MTTR, eliminate alert fatigue, and improve operational efficiency.
Features
- Automatic incident investigation triggered by alerts from webhook-compatible monitoring tools, with root-cause analysis delivered to Slack in under two minutes
- Parallel analysis of pods, services, deployments, logs, events, configurations, and resource relationships to evaluate multiple hypotheses simultaneously
- Living system model that continuously learns service dependencies, changes over time, and accumulating risks across the production environment
- Staging environment monitoring that surfaces recurring issues and proposes targeted fixes before they reach production
- Auto-optimization capabilities that identify over-provisioned resources, scaling issues, and misconfigurations, proposing actionable fixes as pull requests
- Integration with Kubernetes, GitHub, Grafana, and major observability platforms without requiring new agents or dashboards
- SOC2-compliant security with no transfer of PII or sensitive data, and user control over which data is uploaded