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/// automated investigation

Revenue dropped 23%. Here's exactly why.

When a metric changes, askotter checks every source and gives you a ranked list of causes with confidence scores.

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GA Google Analytics SH Shopify ST Stripe ME Meta Ads DATA LAKE 4 sources Automated Root Cause Analysis
/// how investigations work

When something breaks, you get the "why" automatically.

When an anomaly appears, askotter checks your entire data lake. It finds related changes, tests them against historical patterns, and returns ranked causes. Example: "Revenue dropped 23%. Checkout broke after a CSS deploy at 11pm. Confidence: 94%."

Train your best investigator. Scale that knowledge to everyone.

"Why?" is the most expensive question in business. It usually takes a full day of digging. askotter answers it in minutes and shows its work. Save investigation reports as shared notes to build a knowledge base.

/// INVESTIGATION ENGINE
→ Automatic investigation on every anomaly
→ Cross-platform correlation analysis
→ Ranked causes with confidence scores
→ Historical pattern matching
→ Shareable investigation reports
POWERED BY INTEGRATIONS
ROOT CAUSE · INVESTIGATION #RC-0214 6 SOURCES CHECKED
RANKED CAUSES · REVENUE DROP 23% ON FEB 14
94%
Checkout gateway timeout: Stripe incident #4821 caused 143 failed transactions between 11:03 PM and 11:47 PM EST
4%
Mobile CSS regression: Cart page button z-index conflict introduced in deploy v2.4.1 at 10:58 PM
2%
Seasonal traffic variance: Wednesday evening dip within normal historical range (not actionable)
Sources: Stripe · Shopify · GA4 · Meta Ads · Search Console · HubSpot RESOLVED · 4 MIN
/// real-world scenario

Diagnosing a revenue drop in 4 minutes

How automated root cause analysis replaced a day of manual investigation.

11:03 PM
Revenue anomaly detected
23% revenue drop detected. Severity: critical. Investigation begins.
11:04 PM
Cross-referencing all sources
Checking all connected tools for correlated changes in the same window.
11:06 PM
Ranked causes generated
#1: Checkout gateway timeout (94%). #2: CSS regression (4%). #3: Seasonal variance (2%).
11:07 PM
Alert sent with full report
On-call engineer gets the full report. Stripe incident confirmed. Fix deployed by 11:20 PM.
BEFORE vs AFTER
METRIC
BEFORE
WITH ASKOTTER
Time to root cause
4-8 hours manual investigation
4 minutes automatic
Sources checked
1-2 (whoever is debugging)
All connected tools
Confidence scoring
None (best guess)
Ranked with % confidence
Documentation
Slack thread maybe
Saved notes and shareable investigation reports
/// investigation gaps closed

What Takes All Day to Diagnose

Revenue dropped 23%. Get the answer in minutes, not days.

Setup takes under an hour. No engineering required.

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