Most reliability tools watch one box at a time. The Spark UI watches the cluster. The model dashboard watches the model. When the GenAI app slows down, you stitch it together yourself. Unravel traces the work itself, all the way through.
Spark UI shows you today's run. The dashboard shows you a number. Neither tells you what changed since yesterday's run finished in 22 minutes. Unravel diffs every run against its own history — query plan, data volume, infrastructure, code — and surfaces the delta in plain English.
Every category sees one box. Reliability lives in the spaces between them.
Batch breaks at the SLA. Streaming breaks when lag drifts past the budget. GenAI breaks when an agent silently goes off the rails. Arvix watches all three the same way.
Three capabilities every modern data and AI platform needs. What Unravel delivers at each stage — across batch, streaming, training, and GenAI.
No heuristics. No black boxes. No surprises. If a change would break something, Arvix doesn't apply it. That's why customers let Arvix run on AutoApply for 70%+ of actions.
Three capabilities every modern data and AI platform needs. What Unravel delivers at each stage — across batch, streaming, training, and GenAI.
No heuristics. No black boxes. No surprises. If a change would break something, Arvix doesn't apply it. That's why customers let Arvix run on AutoApply for 70%+ of actions.
Bring a recurring SLA miss — we'll trace it end‐to‐end on a 30‐minute call.
Forecasts, root causes, and fixes land in the surfaces engineers already use — Slack, PagerDuty, the PR, the Spark UI. Arvix opens a PR with the proposed fix; the team reviews and merges a diff, the same way they handle any other code change. No new dashboard, no new on-call rotation, no new vocabulary.
Engineering owns reliability because the platform makes prevention the path of least resistance — not because you wrote a runbook nobody reads.
