Production Intelligence
AutoObserve continuously detects and investigates production incidents, correlating telemetry, topology and changes to explain what happened, why it happened and what to do next.
Built for the production stack you already run.
The problem
Metrics, logs, and traces are necessary — but they leave the investigation to humans. AutoObserve turns the same sources into an evidence-backed explanation.
Signals
Metrics · Logs · Traces · Events land in separate tools.
Dashboards
Charts without a shared causal frame.
Alerts
Noise that still requires human triage.
Engineer
Search, correlate, and reconstruct why by hand.
Why?
The answer is still missing when the tools stop.
Evidence
Telemetry, changes, and topology in one investigation.
Investigation
Competing explanations tested against evidence.
What?
What changed in production — with sources attached.
Why?
Root-cause candidate with confidence you can inspect.
Decision
Impact and a recommended next action.
From incident to explanation
AutoObserve decides whether it matters, tests competing explanations, maps impact through the system, and recommends what to do — on the same checkout investigation you saw in the hero.
01 — Detect
AIDDE decides whether a deviation warrants investigation — and what can stay quiet.
AIDDE decision
Decision
● Investigate
Strong evidence of customer-impacting production degradation after a related deployment.
Suppressed
Decision
Suppressed
No evidence of customer impact.
02 — Investigate
Competing hypotheses are ranked against supporting and contradicting evidence — not a chat answer.
03 — Understand
Topology and causal structure show where the failure started and what it touched — Impact, not ownership theatre.
04 — Decide
The investigation consolidates into what happened, why, impact, confidence, and a recommended action.
Investigation engine
AutoObserve plans queries across metrics, logs, and traces — then folds in changes and topology before it ranks explanations.
Ask AutoObserve
Why did checkout latency increase after the checkout-api deployment?
Investigation plan
Metrics
PromQL
Logs
LogQL
Traces
TraceQL
Intent
Explain checkout p95 regression in the window after checkout-api v2.14.7.
Investigation plan
Correlate deploy markers, saturation metrics, failed spans, and dependency topology.
Generated queries
PromQL latency + pool saturation; LogQL acquisition timeouts; TraceQL DB spans for checkout-api.
Source responses
Prometheus, log store, trace backend, and Kubernetes events return raw evidence.
Evidence normalisation
Signals are ranked by correlation and time alignment to the deploy.
Reasoning inputs
Hypotheses are scored from supporting and contradicting evidence — then explained.
Production Intelligence
AutoObserve converts production evidence into understanding, decisions, and eventually learned operational knowledge.
Observe
Telemetry, changes, topology
Detect
Decide what deserves attention
Investigate
Test competing explanations
Understand
Map blast radius and impact
Decide
Evidence-backed next action
Respond
Notify · Recommend · Execute* · Verify*
Learn
Carry patterns forward
* Execute and Verify depend on deployment configuration — represented in the loop, not sold as always-on autonomous remediation.
Built for engineers
OpenTelemetry-native. Kubernetes-aware. Designed to coexist with your observability stack — an intelligence layer above it, not a replacement for it.
$ helm install autoobserve … # partner install guide
Keep the observability workflows you already use. Add an intelligence layer above them. Explore production evidence →