Cleric

Cleric is an autonomous AI Site Reliability Engineer designed to capture and institutionalize the tribal knowledge that emerges from every production incident. Its core value proposition lies in transforming reactive troubleshooting into a proactive, self-improving system. By automatically investigating issues, constructing a detailed knowledge graph of your infrastructure and services, and learning from each event, Cleric ensures that every subsequent investigation is faster and more informed than the last. It acts as a persistent, intelligent layer over your observability stack, aiming to eliminate repetitive manual analysis and prevent the same problems from recurring.

Key features: The platform autonomously triages alerts, correlates events across logs, metrics, and traces, and generates root cause analyses. It can, for example, automatically link a spike in error rates to a specific recent deployment or a downstream API latency degradation. Cleric builds a living knowledge graph that maps service dependencies, historical incidents, and remediation steps. It provides natural language explanations for incidents and can suggest or even execute runbooks. The system also offers collaborative investigation rooms where teams can work alongside the AI to resolve issues.

What sets Cleric apart is its concept of 'production memory'—it is the only AI SRE that retains and applies contextual learnings from past incidents to new, similar situations. This memory allows it to recognize patterns that human engineers might miss. Technically, it leverages advanced NLP to parse unstructured incident data and machine learning to model system behavior. It integrates deeply with popular observability tools like Datadog, New Relic, and Grafana, as well as ticketing systems like Jira and communication platforms like Slack, creating a seamless workflow within existing toolchains.

Ideal for engineering and SRE teams at technology companies, particularly those managing complex, microservices-based architectures who are burdened by alert fatigue and mean time to resolution (MTTR). Specific use cases include e-commerce platforms needing to maintain uptime during sales events, SaaS companies managing multi-tenant environments, and financial services firms requiring rigorous post-incident analysis for compliance. It is valuable for any organization where institutional knowledge is lost due to team turnover or siloed information.

The platform operates on a freemium model, offering a free tier for basic incident capture and analysis for small teams or individual developers, with paid plans unlocking advanced features like unlimited knowledge graph nodes, custom integrations, and team collaboration tools for larger organizations.