AI Variance Investigation System
Deploys 12 specialized AI agents working in parallel to analyze 90 days of transaction history in minutes, detect temporal patterns and correlations, identify multi-location organized retail crime patterns, calculate root cause with Bayesian confidence scoring, and generate prioritized, actionable recommendations—significantly reducing investigation time with high accuracy..
Problem Statement
The challenge addressed
Solution Architecture
AI orchestration approach
Evidence & Findings dashboard showing AI-discovered patterns with confidence scores and LLM reasoning traces
Real-time multi-agent orchestration displaying 12 AI agents analyzing variance patterns with live data streams
Investigation findings with detailed agent analysis results and multi-source evidence correlation
Executive Report with ROI analysis, key findings, and actionable recommendations for variance resolution
AI Agents
Specialized autonomous agents working in coordination
Data Collection Agent
Investigation teams struggle to aggregate data from disparate sources including POS systems, inventory databases, and external feeds, leading to incomplete analysis and delayed investigations.
Core Logic
Automatically connects to multiple data sources, performs ETL processing with data validation and schema mapping. Aggregates transaction records, inventory snapshots, and contextual data into a unified dataset. Serves as the foundation for all downstream analysis agents with validated, structured data.
CCTV Vision Analyst
Security footage review is time-intensive and prone to human error. Key behavioral indicators and suspicious activities often go undetected during manual review of hours of video.
Core Logic
Employs GPT-4 Vision for multi-modal AI analysis of video feeds. Performs object detection, person tracking, behavior recognition, and activity anomaly detection. Identifies concealment gestures, unusual dwell times in high-shrink zones, and correlates video evidence with transaction timestamps.
Transaction Pattern Analyst
Transaction fraud patterns like void clustering, refund anomalies, and discount abuse are difficult to detect manually within large transaction volumes spanning 90+ days of data.
Core Logic
Analyzes transaction patterns using statistical models to detect void/refund clustering, payment anomalies, and suspicious timing patterns. Calculates anomaly scores and identifies employee IDs associated with unusual activity. Cross-references against known fraud pattern signatures.
Temporal Correlation Specialist
Variance events often correlate with specific time windows, shift changes, or schedules, but manual analysis cannot efficiently identify these temporal relationships across large datasets.
Core Logic
Performs time-series analysis correlating variance timestamps with employee shift schedules, store hours, seasonal patterns, and external events. Calculates statistical correlation coefficients to identify shift-based patterns with quantified significance levels.
Cross-Location Intelligence Agent
Organized Retail Crime (ORC) operations target multiple stores in coordinated patterns that single-store analysis cannot detect. Without cross-location visibility, ORC activities continue undetected.
Core Logic
Queries 50+ stores in regional networks to compare variance patterns across locations. Detects multi-store coordinated theft patterns, geographic clustering, and ORC tactical signatures. Calculates pattern match scores against known organized crime tactics in regional crime databases.
Geospatial Intelligence Agent
External factors like regional crime trends, competitor proximity, and geographic risk corridors significantly impact shrinkage but are rarely incorporated into loss prevention analysis.
Core Logic
Analyzes location data, regional crime statistics, competitor proximity, and ORC corridor patterns. Generates risk heatmaps with intensity scoring, identifies route patterns for theft operations, and provides regional benchmarking against comparable stores in similar geographic contexts.
Historical Pattern Analyzer
Without historical context, investigators cannot determine if current variances follow recurring patterns or represent new threats. Similar past incidents and their resolutions are not easily accessible.
Core Logic
Searches 18 months of historical investigation data to find similar incidents and recurring patterns. Performs case comparison, analyzes resolution outcomes and effectiveness, and identifies seasonality factors. Provides historical precedent to strengthen root cause hypotheses.
Predictive Analytics Engine
Reactive loss prevention waits for variances to occur before investigation. Organizations need forward-looking risk intelligence to prevent losses before they happen.
Core Logic
Machine learning-powered prediction engine that forecasts future shrinkage risk, performs loss projections, and identifies anomaly predictions. Conducts what-if analysis for intervention scenarios and provides risk trending with confidence intervals for proactive decision-making.
Real-Time Anomaly Monitor
Traditional batch analysis misses real-time signals. Live transaction streams, sensor data, and operational feeds contain immediate indicators that require instant detection and response.
Core Logic
Monitors live data streams from POS transactions, inventory movements, CCTV analytics, foot traffic sensors, weather feeds, and social media signals. Performs stream processing with threshold monitoring, generates immediate alerts for emerging patterns, and enables real-time intervention.
Root Cause Reasoning Engine
Synthesizing findings from multiple analysis streams into a coherent root cause determination requires expertise in evidence weighting, hypothesis testing, and confidence scoring that varies with investigator experience.
Core Logic
Applies Bayesian inference to synthesize evidence from all analysis agents. Ranks competing hypotheses with posterior probability calculations, generates reasoning chains with evidence links, and produces confidence intervals. Provides explainable AI reasoning for audit trail compliance.
Agent Collaboration Coordinator
Multi-agent systems can produce conflicting findings that require resolution. Without coordination, agent outputs may contradict each other or miss synergies from combined analysis.
Core Logic
Orchestrates multi-agent discussions through structured collaboration protocols. Manages hypothesis debates, resolves conflicting evidence through weighted consensus building, and synthesizes collective intelligence into unified findings with consensus confidence scores.
Action Recommendation Engine
Identifying root cause is only half the solution. Investigators need actionable, prioritized recommendations with implementation roadmaps, resource requirements, and ROI projections.
Core Logic
Generates prioritized action recommendations based on root cause findings. Calculates ROI for each recommendation, estimates implementation effort and resource requirements, provides success probability scoring, and creates step-by-step implementation plans with ownership assignments.
Worker Overview
Technical specifications, architecture, and interface preview
System Overview
Technical documentation
Tech Stack
6 technologies
Architecture Diagram
System flow visualization