Causely provides a causal intelligence layer that converts raw observability telemetry into a real‑time semantic model of system topology and causal relationships. By delivering deterministic, structured context to AI‑based ops agents, it enables faster, more accurate incident detection and remediation while reducing token usage and unnecessary tool calls for SRE and DevOps teams.
Funding
$13.3M 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
Production operations teams rely on AI agents to diagnose and resolve incidents, but these agents often lack a coherent understanding of system causality, leading to slow diagnoses, high token usage, inaccurate root‑cause analysis, and hallucinated incidents.
Solution
Causely provides a causal intelligence layer that transforms raw observability telemetry into a real‑time semantic model of system topology and causal relationships. By supplying agents with structured, deterministic context, Causely enables faster, more accurate incident detection, explanation, and remediation while dramatically reducing token consumption and unnecessary tool calls. The platform continuously infers expected behavior, distinguishes true anomalies from routine variations, and signals when no active incident exists, allowing agents to act proactively and avoid hallucinations. Integration with existing AI agent frameworks is achieved through APIs that deliver the causal graph and state interpretation on demand.
Target Audience
Primary customers are SRE and DevOps teams that deploy AI‑powered incident triage and remediation agents in large‑scale production environments.
Features
- Real‑time construction of causal graphs across service topologies from telemetry data
- Structured environment state representation that agents can query for root‑cause and impact analysis
- Machine‑learning driven anomaly detection that distinguishes genuine incidents from normal variation
- API delivering deterministic causal context to any LLM‑based ops agent, reducing token usage by up to 60%
- Automatic reduction of tool‑call volume by ~4.8×, focusing agent reasoning on resolution steps
- Guarantees 100% root‑cause accuracy in benchmarked scenarios, eliminating hallucinated incidents