Ops Intelligence
Don't read logs. Ask them.
RAG-based RCA over your stack: K8s, AWS, GCP, Datadog. Natural-language root cause.
Datadog shows you what broke. It doesn't tell you why.
An incident hits. You jump between Datadog, CloudWatch, Loki, Grafana, GitHub, the deploy log, and Slack threads from last week. Twenty minutes later you have a theory. Ops Intelligence answers in 30 seconds, in plain language, with the evidence linked.
How it works
- 01Connect your observability stack
Datadog, CloudWatch, Prometheus, Loki, Grafana, Sentry — read-only API keys.
- 02Ask in natural language
"Why did checkout latency spike at 14:32?" The AI builds a RAG query across logs, metrics and traces.
- 03Get a cited answer
Root cause hypothesis with links to the specific log lines, deploy diff, and a recommended fix.
Capabilities
Logs, metrics, traces, deploy history, runbook, and team's Slack incident channel — one query.
Auto-generated timeline of an incident: deploys, alerts, manual actions, all in one view.
Based on past resolutions in your repo, the AI proposes a code or config change.
Generates the first draft of a post-mortem from the incident timeline. Edit and ship.
Understands your error budget and tells you when an investigation can wait until business hours.
Won't hammer Datadog API. Caches and dedups. Predicts query cost before running.
Use cases
"What changed in the last 30 minutes?" One-shot answer with the deploy diff that broke it.
"Top 3 root causes this quarter, grouped by service." Generated as a slide-ready chart.
Asks "what does the payments service do?" and gets a current, code-grounded answer — not a stale wiki.
Frequently asked
Ready to see it run?
We deploy a pilot in a day. Hebrew-native, EHR-aware, ROI-measurable.
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