Meet Sherlock

Elementary, My Dear User

Sherlock doesn't just tell you what changed—he investigates why. With the precision of a forensic scientist and the rigor of statistical analysis, Sherlock separates correlation from causation, noise from signal, and guesswork from certainty.

"Aha! The root cause is elementary—it's the checkout flow, Watson. 87% confidence."- Sherlock's analytical style

Wearing his signature spectacles and deerstalker cap, Sherlock examines every data point with obsessive attention to detail. No hypothesis goes untested. No assumption goes unchallenged. The truth always reveals itself under his magnifying glass.

Quick Validation

⏱️ <1 minute

Rapid sanity checks and immediate hypothesis testing. Perfect for confirming or rejecting obvious causes during active incidents.

Standard Analysis

⏱️ 5-10 minutes

Comprehensive causal investigation with statistical rigor. Identifies root causes with confidence scores and validates data quality.

Deep Forensics

⏱️ 30+ minutes

Exhaustive forensic analysis for complex scenarios. Multi-factor causation, Bayesian inference, complete evidence chains, and peer-reviewed statistical validation.

Forensic Analysis

Performs rigorous root cause investigations using causal inference techniques like Granger causality and Pearl's do-calculus

Statistical Validation

Tests hypotheses with p-values, confidence intervals, and statistical significance to eliminate false positives

Data Quality Assessment

Validates data completeness, accuracy, and timeliness across all integrations, flagging quality issues

Causal Chain Mapping

Constructs detailed cause-and-effect diagrams showing exactly how one change led to another

Noise Elimination

Separates signal from noise, identifying true causes while filtering out coincidental correlations

Integration Health Monitoring

Continuously validates API connections and data flows, detecting sync issues before they affect insights

Churn Source Identified

Root cause: Mobile checkout flow update (not seasonality or pricing)

Eliminated alternative hypotheses: seasonal patterns (historical data shows no October dip), pricing (no changes), competition (market stable). Statistical significance: 99.9%.

Email Timing Optimization

Churn reduction: Onboarding email #3 timing is causal factor

Hypothesis: Users need 48 hours to experience core value before email resonates. Earlier timing triggers dismissal.

Integration Data Gap

⚠️ DATA QUALITY: Shopify missing 18% of timestamp data—attribution affected

Recommended: Re-sync Shopify data with timestamp backfill. Pause attribution reports until resolved. ETA: 3 hours.

Trigger Reception

Receives investigation requests from Echo alerts, user queries, or scheduled data audits

Evidence Gathering

Collects all relevant data points, timestamps, and historical context from unified data lake

Hypothesis Testing

Applies statistical tests, causal inference algorithms, and Bayesian analysis to validate causes

Case Closed

Delivers conclusive findings with confidence scores, evidence chains, and actionable recommendations

Execution Cadence

On-Demand

Triggered by alerts or user queries

Analysis Speed

• Level 1: <1 minute
• Level 2: 5-10 minutes
• Level 3: 30+ minutes

Data Requirements

• Works with available data
• Handles incomplete datasets
• Validates quality in process

Core Technologies

• Causal inference (Granger, Pearl's do-calculus)
• Hypothesis testing (p-values, t-tests)
• Bayesian inference
• Data quality scoring

Output Types

• Root cause analysis reports
• Data quality assessments
• Causal chain diagrams
• Validation reports

Performance Metrics

• Root cause accuracy: >90%
• False positive rate: <5%
• Data quality detection: 99%

When Echo detects an anomaly...

When Finn finds a pattern...