Provides an AI-powered platform for autonomous cloud application management, using causal reasoning to automate root cause analysis and prevent SLO violations. It integrates with existing observability tools to identify and mitigate reliability risks in real time, ensuring consistent application performance without manual troubleshooting.
Funding
$10.8M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Modern cloud-native applications are complex, making root cause analysis of performance issues and service level objective (SLO) violations difficult and time-consuming. Existing observability tools often overwhelm teams with data but fail to provide clear, actionable insights, leading to prolonged troubleshooting and business disruptions.
Solution
Causely offers an AI-powered causal reasoning platform that automates root cause analysis and proactively prevents SLO violations in complex cloud environments. By integrating with existing observability stacks, Causely automatically maps application topology and service dependencies to identify cause-and-effect relationships in real-time. The platform's causal AI engine infers the true root cause of incidents, even in noisy environments, and predicts future failures based on the customer's topology. Causely provides a high-signal, low-noise view of critical issues, enabling DevOps and SRE teams to resolve incidents faster, reduce downtime, and maintain application reliability. Users can interact with Causely’s analytics via the web UI, API, and webhooks, and the platform integrates directly with tools like Grafana and Splunk OnCall.
Target Audience
Causely targets DevOps and Site Reliability Engineering (SRE) teams, service owners, software engineers, and platform operations teams responsible for maintaining the reliability and performance of cloud-native applications.
Features
- Automated root cause analysis using causal Bayesian networks and principled causal reasoning
- Proactive SLO violation prevention by identifying and acting on SLOs at risk
- Automatic topology mapping and service dependency discovery
- Integration with existing observability tools via auto-instrumentation
- "AskCausely" copilot experience for zero-click insights and next-step guidance in Slack, Grafana, and Microsoft Teams
- Configurable latency and error thresholds per service
- Support for Splunk OnCall for incident response with root cause identification
- Improved anomaly detection and diagnosis for Amazon Web Services (AWS) Application Load Balancers (ALBs)
- Web UI, API, and webhooks for interacting with Causely’s analytics