OverviewPrediction Engine

Prediction Engine

Intelligence that sees what's coming

How It Works

Step through the process to see how it works.

1

Historical Data Collection

The engine gathers historical event patterns, entity behaviors, and outcome data to build a predictive baseline.

Prediction Engine Pipeline

Historical Data

Now
Recidivism Scoring
Network Reactivation
Spatial Forecasting
Temporal Patterns

Predictions

Attack Probability

73%

Timeline

7-14 days

Confidence

82%

Risk Trend

Rising

Step 1 of 6

Key Features

Core capabilities that power this module.

Recidivism Scoring

Predict likelihood of re-offense based on criminal history patterns, network activity, and behavioral indicators.

Network Reactivation

Detect when dormant networks resume operational communication patterns — early warning of cell reactivation.

Spatial Forecasting

Predict incident hotspot emergence based on geographic clustering patterns and historical spatial data.

Temporal Patterns

Identify cyclical and seasonal threat patterns to optimize resource allocation and operational readiness.

Scenario: Cell-7 Reactivation Alert

A realistic walkthrough using fictional intelligence data.

The prediction engine detects a pattern match for network reactivation in Cell-7, a dormant cell flagged 8 months ago. The system generates a high-confidence alert before any operational activity is detected.

1Rashid Patel (released 6 months ago) receives a 78% recidivism score — re-establishing Cell-7 communication links
24 dormant Cell-7 members resume contact within a 14-day window — matching the pre-operational communication signature
3Cell-7 network reactivation probability calculated at 65% — exceeding the 50% alert threshold
4System generates a prediction report with contributing signals: CDR resumption, financial activity, location convergence
5Analyst marks prediction as "Accurate" two weeks later — feedback improves model for future detections
AI Analysis EngineBack to Overview

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