See issues before they break things, fix them faster when they do
Causely continuously models cause-and-effect across your system to surface emerging risks, pinpoint true root causes, and guide safe remediation directly in your workflow.
From raw telemetry to real causal understanding
Causely distills your telemetry into clear, trusted causal insight, showing what changed, why it matters, and the actions to resolve or prevent issues all directly in your workflow.
Understand system behavior
Causely builds a continuous causal model of your environment, mapping how services and dependencies influence one another. Move beyond brittle dashboards to a shared, principled understanding of behavioral ripple effects.
Map system-wide behavior propagation- Explain downstream impact of changes
- Detect emerging reliability risks early
Release changes with confidence
Causely models how code, config, and dependency changes will ripple through your system. See the potential impact of changes early and eliminate the risk of surprise breakage.
- Catch regressions pre-production
- Understand downstream effects
- Ship safely without surprises
Focus on what really matters
When symptoms flood multiple services, Causely uses causal inference to pinpoint the single upstream trigger. Shift from reactive triage in a crisis to executing fast, confident resolutions based on a clear explanation of behavior.
- Identify the true upstream trigger
- Cut through noise and alert storms
- Reduce time to detect and resolve
How we turn data into intelligence
Causely uses a model-driven reasoning engine to infer root causes, working alongside your existing stack to interpret data meaning, not replace it.
Connect telemetry with the Causely mediator
Turns structure and domain knowledge into a real causal reasoning engine.
Generate a dynamic dependency graph
Turns structure and domain knowledge into a real causal reasoning engine.
Live map of services, infra, and dependencies.
Definitions of entities, failure modes, and constraints.
Build your causal model
Turns structure and domain knowledge into a real causal reasoning engine.
Bayesian network modeling cause-and-effect paths.
Captures functional relationships and cascades.
Run continuous causal inference
Identifies the true cause from noisy signals and prioritizes what threatens SLOs.
Predict and prevent failure
Forecasts likely failure paths and triggers guardrails or actions to stay ahead of incidents.
Boost speed across your org
Causely is built for teams that need to maximize velocity while guaranteeing reliability.
Incident response
Resolve incidents faster with real-time, explainable root cause, blast radius, and fix actions.
Developers
Accelerate dev velocity with clear, specific remediation steps right in your workflow (Jira/IDE).
Engineering leadership
Shift your team's focus from reactive fire-fighting to high-value feature development.
Platform engineers
Proactively catch subtle warnings and regressions before code moves to production.
Auto-discovery
Auto-instruments traces and discovers system topology (services, APIs, databases) out of the box.
No code changes
Seamless ingestion
Your environment, your control.
Causely runs on a split architecture to ensure maximum data privacy and control over your environment.
Data minimization
Raw source data is retained in your environment and never transmitted to the SaaS backend—only distilled symptom state.
Zero sensitive data storage
We don't store or process any sensitive data or PII, and all transmitted data is encrypted both in transit and at rest.
Minimal privilege by default
The mediation components operate with minimal privileges, while specialized components can be controlled or disabled.
BYOC
Teams have the option of a bring-your-own-cloud model, where the entire application runs in your environment.
Move from reactive to autonomous reliability
Experience how causal inference transforms observability data into self-healing systems. Sign up for free today, or connect with our team to see how Causely can work for you.