Cleric is an AI‑powered Site Reliability Engineer that automatically investigates production alerts, builds a real‑time map of services and dependencies, and delivers evidence‑backed root‑cause diagnoses directly to messaging platforms like Slack. By testing hypotheses against logs, metrics, traces and historical incident data, it provides transparent reasoning that engineers can verify and use to ship fixes faster, while continuously learning to improve future investigations.
Funding
$4.3M 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
Software teams spend excessive time manually debugging production alerts, often juggling multiple tools and lacking a clear, trusted root cause, which leads to prolonged outages and reduced engineering productivity.
Solution
Cleric is an AI‑powered Site Reliability Engineer that automatically investigates alerts as soon as they fire. It builds a real‑time map of services, dependencies, and ownership, then forms and tests hypotheses using logs, metrics, traces, and historical incident data. The system presents an evidence‑backed diagnosis directly in the team’s messaging platform (e.g., Slack), showing the reasoning path so engineers can verify the conclusion. Over time Cleric learns from each investigation, extracting reusable diagnostic patterns that improve future analyses across services and teams. The solution runs in a read‑only, secure deployment with no agents, ensuring no sensitive data leaves the environment.
Target Audience
Cleric is aimed at SRE and DevOps teams, as well as software engineering groups that manage complex, microservice‑based production environments and need faster, trustworthy incident resolution.
Features
- Live system mapping that ingests logs, metrics, traces, Kubernetes state, and internal docs to maintain an up‑to‑date service dependency graph
- Hypothesis‑driven investigations with a transparent hypothesis tree that captures reasoning and confidence levels
- Automatic, real‑time root‑cause diagnosis delivered to Slack (or other messaging platforms) with supporting evidence
- Continuous learning that extracts diagnostic patterns from each incident and applies them across services without manual retraining
- Secure, read‑only integration via APIs, requiring no code changes or agents and keeping all data within the customer’s environment
- Ability to reference prior incident resolutions, enabling knowledge reuse and faster fixes for recurring issues