Campaign Performance Analyzer
Deploys 6 specialized AI agents that perform enterprise-grade analysis including statistical anomaly detection, causal inference (not just correlation), strategy generation with Monte Carlo simulation, and risk-validated recommendations with confidence intervals..
Problem Statement
The challenge addressed
Solution Architecture
AI orchestration approach
AI Agent Orchestration with 6 specialized agents performing causal inference and root cause analysis on campaign data
Tool Calling & Execution interface showing complete input/output traceability with detailed agent audit trail
AI Analysis Results identifying weather-driven underperformance with actionable recommendations and impact projections
Real-Time Market Intelligence dashboard with weather impact, competitor analysis, programmatic DOOH, and attribution metrics
AI Agents
Specialized autonomous agents working in coordination
Orchestration Agent
Multi-phase analysis workflows require coordination across data collection, pattern detection, causal inference, strategy generation, and risk assessment agents while maintaining audit trails and handling errors.
Core Logic
Coordinates 6 specialized agents using GPT-4 Turbo (128K context). Manages workflow execution across 6 phases with progressive status updates. Handles agent delegation, error recovery, and resource allocation. Maintains complete audit trails with input/output hashes. Generates multi-audience reports (executive, technical, business) upon completion.
Data Collection Agent
Campaign analysis requires aggregating data from multiple real-time and batch sources with different schemas, latencies, and quality levels while ensuring data freshness and completeness.
Core Logic
Connects to 6+ data sources using GPT-3.5 Turbo for efficient processing. Ingests DOOH impression streams (847K+ records), footfall analytics, weather data, campaign databases, event calendars, and transit disruption feeds. Validates schemas using JSON Schema Draft 7. Handles missing values and ensures data quality assessment before downstream processing.
Pattern Analysis Agent
Detecting performance anomalies in large datasets requires sophisticated statistical methods. Simple threshold-based alerts miss complex patterns and generate false positives.
Core Logic
Applies Isolation Forest algorithm for outlier detection with configurable contamination thresholds. Performs Z-score normalization across metrics. Identifies locations with performance significantly below expected values (>2σ). Detects clustering patterns suggesting systematic issues. Generates statistical test results (t-tests, chi-square) with p-values and effect sizes.
Causal Inference Agent
Correlation analysis cannot distinguish true causes from confounded relationships. Making optimization decisions based on correlations alone leads to ineffective interventions.
Core Logic
Constructs Directed Acyclic Graphs (DAGs) using PC Algorithm with Fisher Z conditional independence tests. Identifies causal pathways distinguishing true causes from confounders. Performs instrumental variable analysis and propensity score matching. Outputs causal relationships with confidence levels, explicitly noting limitations of observational causal inference.
Strategy Generation Agent
Generating optimization strategies requires evaluating multiple scenarios with uncertainty quantification. Single-point estimates cannot capture the range of possible outcomes.
Core Logic
Runs Monte Carlo simulations (10,000 iterations) using Latin Hypercube Sampling. Evaluates candidate strategies across multiple objectives. Performs sensitivity analysis to identify key drivers. Generates recommendations ranked by expected value with confidence intervals. Provides alternatives with trade-off analysis explaining why non-recommended options were rejected.
Risk Assessment Agent
Implementing optimization recommendations without risk validation can lead to adverse outcomes. Strategy changes need stress testing against adverse scenarios before execution.
Core Logic
Performs Value at Risk (VaR) calculations at 95% confidence. Stress tests recommendations against 5+ adverse scenarios (weather changes, competitor surges, capacity constraints). Quantifies risk factors with probability and impact scores. Generates mitigation strategies with effectiveness ratings. Produces rollback plans for reversible actions.
Worker Overview
Technical specifications, architecture, and interface preview
System Overview
Technical documentation
Tech Stack
6 technologies
Architecture Diagram
System flow visualization