Instantly see the single cause behind the symptoms
Continuous application reliability with Causal AI
Trusted by teams who can't afford downtime
In complex microservices environments, failures spread fast
Modern systems are owned by many teams, so when an incident starts, symptoms appear everywhere at once. Latency increases, errors spread, and alerts pile up across services. While it is easy to see that something is broken, it is much harder to know what to chase first. Teams lose valuable time debating where the issue started and who should own the fix.
Symptoms everywhereSymptoms
everywhere
Downstream services fail even when the real issue lies elsewhere.
Too many teamsToo many
teams
Incidents pull in engineers who are not actually needed.
Constant changeConstant
change
AI-assisted development and frequent releases mean systems change faster than teams can keep up, shifting how failures spread every day.
Observability shows impact, not causeObservability shows
impact, not cause
Metrics, logs, and traces reveal symptoms, but teams still struggle to find the source.
From chaos to causality – Causely points teams to the real problem
Causely builds a live causal model of how your services depend on each other and how failures move through your system. When something goes wrong, it helps teams see where the problem started, understand why other services are affected, and quickly identify which team owns the fix.
Instead of starting every incident from scratch, teams begin with a clear answer and move straight to resolution.
Applying AI to observability in complex environments
Instead of generating guesses from past data, Causely reasons over a causal model of your environment to explain what is happening, both during incidents and before changes ship, so teams can meet SLOs, stay aligned, and deliver faster.
Resolve incidents faster
Surface root causes before investigation begins.
Works with imperfect data
Get answers even when telemetry is uneven or incomplete.
Prevent issues before impact
Apply the same understanding in pre-production to catch risky changes.
Avoid revenue loss
Prevent SLO violations before they impact customers, protecting both user experience and income.
"If you're serious about automating reliability in microservices, you need what Causely is doing. Language models are powerful, but they can't make the right calls without structured causal context. That's the gap Causely fills, and it's what makes real-time automation possible."
Karthik Ramakrishnan
VP Artificial General Intelligence
Build reliable systems that run themselves
Get from observability data to autonomous reliability in minutes.
Connect your telemetry
Use metrics, traces, and logs from your existing tools like OTel, Datadog, Prometheus, and more.
Generate your graph
Causely automatically builds a live model of your dependencies and system dynamics in seconds.
Get causal insights
Receive the exact root cause of your symptoms, location, and solution, cutting triage from hours to seconds.
Predict & prevent
Get actionable insights to prevent future incidents and improve your system’s reliability.
Connect your telemetry
Use metrics, traces, and logs from your existing tools like OTel, Datadog, Prometheus, and more.
Generate your graph
Causely automatically builds a live model of your dependencies and system dynamics in seconds.
Get causal insights
Receive the exact root cause of your symptoms, location, and solution, cutting triage from hours to seconds.
Predict & prevent
Get actionable insights to prevent future incidents and improve your system’s reliability.
