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See VeloOps working, before you sign up

This is the real product console running on sample data for a fictional platform team. Trigger an incident and watch it get correlated, toggle smart alerting on and off, connect a source — no account, nothing to install.

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Sample data

Monitoring overview

MTTR, alert volume and the services generating the most noise.

Last 30 days

Mean time to resolution

9m

-63%vs previous 30 days

Active incidents

2

-5right now

Alerts auto-grouped

87%

+21 ptsof noisy alerts merged

Monitored uptime

99.97%

+0.04 ptsvs previous 30 days

Mean time to resolution

Weekly MTTR in minutes, twelve-week trend

-33 min
025W1W3W5W7W9W11W12
Weekly mean time to resolution over twelve weeks, falling from 42 minutes to 9 minutes. Values: W1 42 min, W2 38 min, W3 36 min, W4 33 min, W5 30 min, W6 27 min, W7 24 min, W8 21 min, W9 18 min, W10 14 min, W11 11 min, W12 9 min.

How alerts were handled

Auto-grouped, manually triaged, or suppressed as a duplicate

W5W6W7W8W9W10W11W12
  • Auto-grouped440
  • Manually triaged142
  • Duplicate suppressed580
Alert outcomes by week, split by auto-grouped, manually triaged and suppressed duplicate. W5: Auto-grouped 34, Manually triaged 28, Duplicate suppressed 51. W6: Auto-grouped 41, Manually triaged 24, Duplicate suppressed 58. W7: Auto-grouped 47, Manually triaged 21, Duplicate suppressed 64. W8: Auto-grouped 53, Manually triaged 19, Duplicate suppressed 69. W9: Auto-grouped 58, Manually triaged 16, Duplicate suppressed 75. W10: Auto-grouped 64, Manually triaged 14, Duplicate suppressed 81. W11: Auto-grouped 69, Manually triaged 11, Duplicate suppressed 88. W12: Auto-grouped 74, Manually triaged 9, Duplicate suppressed 94

Noisiest services

Highest raw alert volume, with the share VeloOps correlated automatically

  • checkout-api214
    92% correlated
  • orders-db156
    81% correlated
  • payments-gateway118
    97% correlated
  • notification-queue84
    74% correlated
  • auth-service61
    88% correlated
  • search-index39
    66% correlated

Runbook gaps

Services with no runbook, ranked by how often anomalies hit them

4 to write
  • inventory-sync17
  • image-resizer11
  • billing-webhook8
  • cdn-edge-cache5

Why this matters: a service with no runbook is the one place correlation alone is not enough — someone still has to write down what "normal recovery" looks like.

Demo runs entirely in your browser on sample data for a fictional platform team. Timelines are scripted illustrations of product behaviour, not live model output, and nothing you click sends data anywhere.

Under the hood

What happens between the anomaly and the alert

VeloOps correlates before it explains. That order is what makes root-cause summaries trustworthy — and what lets it say 'this is external, not yours' instead of guessing.

  1. STEP 1

    Telemetry streams in

    Logs, metrics and deploy events flow in continuously from CloudWatch, Datadog, or your existing pipeline — no batch delay.

  2. STEP 2

    A baseline flags the anomaly

    Each service has a learned baseline for normal behaviour. A deviation is flagged the moment it crosses that baseline, not a static threshold.

  3. STEP 3

    Evidence is correlated and scored

    Deploys, infra changes and dependency failures in the same window are pulled together, and a foundation model explains the likely cause with a confidence score attached.

  4. STEP 4

    Notify, suppress, or escalate

    Related alerts are grouped into one incident and routed to the right channel. Duplicate noise for the same failure is suppressed automatically.

The step most tools skip: scoring how well the correlated evidence actually explains the anomaly. Without it, a monitoring tool answers everything with equal confidence — including the failures it has no real evidence for. Try the third-party-outage scenario in the incident console to see the honest version.

What to try

A short tour, in four tabs

Everything above is interactive. Here is what is worth clicking in each section.

Incident console

Pick a scenario and click Inject anomaly. Watch detection, correlation and routing play out, then mark it resolved and see the MTTR.

Dashboard

Hover the MTTR chart and the alert-outcome columns. Read the runbook-gaps panel — it is the documentation backlog, ranked by anomaly volume.

Smart alerting

Flip the grouping switch off, then on. Same failure, seven pages versus one — this is the alert-fatigue fix in one click.

Integrations

Click Connect on Microsoft Teams or OpenSearch and watch the one-click flow — this is what "live in minutes" actually looks like.

Run this on your own services

Connect a service and VeloOps will show you the anomalies it catches and the root-cause summaries it generates against your real traffic.

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