Causal AI that explains root causes instead of piling up alerts
Obseria Intelligence links topology, telemetry and deployments into a causal model, predicts problems and orchestrates agents for prevention, remediation and optimization
Why correlation is not enough: causal AI for IT operations
How Obseria Intelligence determines root causes with explainable evidence.
Read nowAnswers instead of guesses
Three kinds of AI work together: causal, predictive and generative. Every answer is traceable.Causal root cause analysis
Instead of statistical correlations, Obseria Intelligence follows the dependency graph of your systems and names the cause with evidence: which deployment, which configuration, which host.
Predictions and prevention
Detect capacity bottlenecks, certificate expirations and seasonal load peaks days in advance and trigger preventive workflows.
Agentic operations
Built-in and custom agents investigate problems, propose fixes and execute them after approval. Ask in natural language, get answers with evidence.
90% lower MTTR
Helios Bank cuts time to resolution from hours to minutes.
Read the Helios story98% fewer alerts
Kestrel Mobility condensed 40,000 events per day into an average of 9 problems.
Read the Kestrel story7 days lead time on capacity bottlenecks
Northwind Energy scales storage and clusters ahead of seasonal peaks.
Read the Northwind story0 black-box decisions
Granit Public meets accountability requirements because every AI answer contains evidence.
Read the Granit storyEmpower your business with AI-driven observability
Harness trusted answers instead of alert floods, for measurable business outcomes at scale
Explore the Obseria approachFrom signal to answer in three steps
- Step 1
Map dependencies
Obseria TopoGraph builds a live model of services, processes, hosts, clouds and deployments.
- Step 2
Reason causally
Agents test hypotheses along the graph and keep only causes they can prove with telemetry.
- Step 3
Act with guardrails
Proposed fixes are simulated against SLOs and executed only within your approval policies.
What's included
- Causal root cause with evidence chains
- Multi-dimensional anomaly detection
- Capacity and certificate forecasting
- Natural language questions with OQL
- MCP server, CLI and API for your agents
- No customer data used for training
Explore capabilities in detail
Products for production operations
Infrastructure Observability
Automatically discover and monitor hosts, containers, Kubernetes and multi-cloud.
Application Observability
Distributed tracing with code-level context for every transaction.
Log Analytics
Store logs without schemas or indexes and analyze them in seconds.
Digital Experience
Real user monitoring, session replay and synthetic monitoring.
AI Observability
Monitor LLMs, agents and RAG pipelines with cost, quality and guardrails.
Obseria Intelligence
Causal AI for root cause analysis, predictions and automated remediation.
Use cases with Obseria Intelligence
The cause in seconds, with proof
Obseria Intelligence follows the dependency graph from symptom to cause and shows the chain of evidence: deployment at 14:21, memory rise at 14:28, errors from 14:32.
Explore the platformBaselines that understand seasonality
Multi-dimensional baselines learn daily, weekly and yearly patterns. Anomalies are scored by business impact, not statistical deviation.
Infrastructure ObservabilityAsk your telemetry
"Why has checkout been slower since the last release?" Obseria Intelligence translates into OQL, runs the analysis and explains the result with links to traces and logs.
For developersYour own agents on your telemetry
Via MCP server, CLI and API, your own agents and coding assistants access the same data as humans. Obseria orchestrates built-in and external agents with guardrails.
Explore AutomationWorks with your stack
Connect the technologies you already run. 700+ integrations, discovered and put into context automatically.
Leading organizations that run on Obseria Intelligence
Get started with Obseria Intelligence training and documentation
Obseria Intelligence FAQ
Machine learning finds statistical relationships. Causal AI uses knowledge about dependencies (topology, call chains, deployments) to determine what caused a problem, not just what happened at the same time. That makes answers explainable and reliable.
No. Causal analysis works from day one based on the automatically discovered topology. Baselines for anomalies learn within a few days.
No. Customer data is not used to train models and does not leave the selected region.
Yes. Via the MCP server, CLI and API, your own agents and assistants use the same data. Which actions agents may perform is controlled by policies.
Ready for observability that delivers answers?
Take the first steps toward reliable operations