Automated Analyst

The Problem with On-Demand Analysis

Traditional analytics, including conversational AI, only works when someone asks a question. What happens when no one’s asking?

Anomalies go undetected
Opportunities slip by
Problems compound until they’re crises
Patterns emerge and disappear unseen

Superatom’s Automated Analyst runs continuously, monitoring your data, surfacing insights, and alerting you to what matters, even at 3 AM on a Sunday.

The Automated Analyst System

Core Capabilities

1. Continuous Monitoring

The system watches key metrics automatically:

What’s Monitored	How Often	Alert Conditions
Revenue trends	Hourly	> 10% deviation from forecast
Inventory levels	Real-time	Below safety stock
Margin metrics	Daily	Negative or declining
Customer behavior	Daily	Churn risk indicators
Operational KPIs	Configurable	Threshold breaches

2. Anomaly Detection

Detection Methods:

Statistical outlier detection (z-score, IQR)
Time-series decomposition (trend, seasonality, residual)
Machine learning pattern recognition
Domain-specific rules (tribal knowledge)

3. Causal Analysis

When something changes, the system automatically investigates why:

Variable Tracking
Dependency Mapping
Counterfactuals

Track which variables changed when outcomes changed:

Revenue dropped 15% last week

CAUSAL ANALYSIS:
- Order volume: -8% (contributing -12% to revenue)
- Average order value: -5% (contributing -3% to revenue)
- Return rate: +2% (negligible impact)

ROOT CAUSE: Order volume decline
DRILL DOWN: New customer acquisition -40%

What would have happened if…?

COUNTERFACTUAL ANALYSIS:

Actual revenue: 1.2M

IF marketing had stayed constant:
→ Estimated revenue: 1.38M
→ Impact: 180K loss from marketing reduction

IF conversion rate improved 1%:
→ Estimated revenue: 1.25M
→ Potential gain: 50K

4. Knowledge Nodes

The Automated Analyst uses Knowledge Nodes, fundamental building blocks that define how computations work: Each node knows:

How to calculate its value
What it depends on
When to alert
How to explain changes

Automated Actions

Action Types

Action	Description	Example
Daily KPI Summary	Executive summary of business health	Every morning at 7 AM
Anomaly Alert	Immediate notification of issues	Revenue drop > 10%
Trend Warning	Emerging patterns that need attention	3 consecutive weeks of margin decline
Opportunity Signal	Positive patterns to capitalize on	Product X sales surging in Region Y

Action Configuration

automated_action:
  name: "Revenue Anomaly Detection"
  type: "financial_intelligence"

  trigger:
    metric: "daily_revenue"
    condition: "variance > 15% OR variance < -10%"

  schedule:
    frequency: "hourly"
    active_hours: "24/7"

  actions:
    - generate_dashboard
    - send_email_alert
    - create_investigation_task

  recipients:
    - role: "Finance Team"
    - user: "[email protected]"

  intelligence:
    - include_causal_analysis: true
    - include_recommendations: true
    - historical_context: "30 days"

Time-Based Intelligence

Temporal Query Understanding

The Automated Analyst understands time in business context:

Query Type	Example	System Behavior
Point-in-time	”What was inventory on Jan 1?”	Snapshot query
Period comparison	”Compare Q4 to Q3”	Fiscal-aware comparison
Trend analysis	”How has margin trended?”	Time-series with seasonality
Forecast	”What will revenue be next month?”	Predictive modeling
Counterfactual	”What if we hadn’t raised prices?”	Causal inference

Seasonality Awareness

The system knows that a December sales spike isn’t an anomaly, but a December spike that’s 30% below last December IS.

Always-On Intelligence

The system gets smarter over time:

False positive alerts? Thresholds adjusted.
Missed important events? Detection rules refined.
User feedback incorporated into future analysis.

Business Impact

From Reactive to Proactive

Traditional	With Automated Analyst
Discover problems in monthly review	Know about problems in minutes
Investigate manually when asked	Root cause analysis automatic
Miss patterns between reports	Continuous pattern detection
Rely on analyst availability	24/7 monitoring

ROI Examples

Early Anomaly Detection

Detected inventory discrepancy 3 days earlier than manual review would have.Saved: 45K in potential stockout costs

Margin Erosion Alert

Identified supplier cost increase affecting margins before quarterly close.Action: Renegotiated contract, recovered 2% margin

Opportunity Surfacing

Detected regional demand surge for understock product.Result: Expedited shipment, captured 120K additional revenue

Time Savings

Automated daily KPI summary replaces 2 hours of analyst work.Annual savings: 500+ analyst hours

The Complete Vision

The Automated Analyst is the final piece that closes the decision loop: Top leaders in the organization can just ask questions and get insights. We connect it to action systems. And it runs in a loop.

Getting Started

Product

Architecture

Security & Data Privacy

Intellectual Property

Implementation

Use Cases

Case Studies

API Reference

Guides

The Problem with On-Demand Analysis

The Automated Analyst System

Core Capabilities

1. Continuous Monitoring

2. Anomaly Detection

3. Causal Analysis

4. Knowledge Nodes

Automated Actions

Action Types

Action Configuration

Time-Based Intelligence

Temporal Query Understanding

Seasonality Awareness

Always-On Intelligence

Business Impact

From Reactive to Proactive

ROI Examples

Early Anomaly Detection

Margin Erosion Alert

Opportunity Surfacing

Time Savings

The Complete Vision

Next Steps

Platform: Actions

Security Overview

​The Problem with On-Demand Analysis

​The Automated Analyst System

​Core Capabilities

​1. Continuous Monitoring

​2. Anomaly Detection

​3. Causal Analysis

​4. Knowledge Nodes

​Automated Actions

​Action Types

​Action Configuration

​Time-Based Intelligence

​Temporal Query Understanding

​Seasonality Awareness

​Always-On Intelligence

​Business Impact

​From Reactive to Proactive

​ROI Examples

Early Anomaly Detection

Margin Erosion Alert

Opportunity Surfacing

Time Savings

​The Complete Vision

​Next Steps

Platform: Actions

Security Overview

The Problem with On-Demand Analysis

The Automated Analyst System

Core Capabilities

1. Continuous Monitoring

2. Anomaly Detection

3. Causal Analysis

4. Knowledge Nodes

Automated Actions

Action Types

Action Configuration

Time-Based Intelligence

Temporal Query Understanding

Seasonality Awareness

Always-On Intelligence

Business Impact

From Reactive to Proactive

ROI Examples

The Complete Vision

Next Steps