AI Trade Promotion Optimizer
Deploys an 8-agent orchestrated AI system that retrieves historical data via RAG, analyzes patterns, generates ML predictions using XGBoost/LightGBM ensemble with 847 engineered features, assesses risks, gathers market intelligence, optimizes parameters using NSGA-III multi-objective algorithms for Pareto-optimal solutions, and generates retailer-specific campaign briefs with executive summaries..
Problem Statement
The challenge addressed
Solution Architecture
AI orchestration approach
Campaign Configuration - Input form for High ROI Spring Campaign with product selection and 8-agent pipeline workflow
Agent Orchestration - Real-time multi-agent workflow execution with 8 specialized agents collaborating on promotion optimization
Campaign Optimization Results - AI-generated recommendations with 3.7x projected ROI, $540K revenue, and optimal configuration
Historical Insights Dashboard - Predictive analytics with 2,847 promotions analyzed and performance by discount analysis
AI Agents
Specialized autonomous agents working in coordination
Supervisor Agent
Complex multi-agent workflows require coordination, quality assurance, and proper sequencing to ensure reliable outputs.
Core Logic
Orchestrates workflow execution using Claude 3.5 Sonnet (temperature 0.1), managing agent handoffs, validating outputs, and ensuring quality standards are met throughout the analysis pipeline.
Data Retrieval Agent
Historical promotional data is scattered across multiple systems and requires intelligent retrieval for comprehensive analysis.
Core Logic
Leverages GPT-4 Turbo (temperature 0.0) with RAG-based data acquisition to query vector stores, retrieve relevant historical campaigns, and aggregate data from syndicated sources.
Historical Analysis Agent
Raw historical data requires pattern recognition to identify successful promotion strategies and seasonal trends.
Core Logic
Uses Claude 3.5 Sonnet (temperature 0.2) to analyze historical patterns, identify successful promotion characteristics, and extract actionable insights from past campaign performance.
Predictive Modeling Agent
Accurate prediction of campaign outcomes requires sophisticated ML models with extensive feature engineering.
Core Logic
Employs XGBoost/LightGBM ensemble models with 847 engineered features to predict promotional lift, ROI, and volume outcomes with high accuracy.
Risk Assessment Agent
Trade promotions carry compliance, financial, and execution risks that must be identified and mitigated proactively.
Core Logic
Applies Claude 3.5 Sonnet (temperature 0.1) to evaluate compliance requirements, assess financial exposure, and generate risk mitigation strategies for each campaign recommendation.
Market Intelligence Agent
Competitive dynamics and market trends significantly impact promotion effectiveness but are difficult to monitor continuously.
Core Logic
Utilizes GPT-4 Turbo (temperature 0.3) to analyze competitive promotions, monitor market trends, and incorporate external factors into campaign recommendations.
Optimization Agent
Finding optimal campaign parameters requires balancing multiple competing objectives including ROI, revenue, and risk.
Core Logic
Implements NSGA-III multi-objective optimization algorithms to generate Pareto-optimal campaign parameters that balance discount depth, timing, and expected outcomes.
Content Generation Agent
Translating analytical outputs into actionable retailer-specific documentation is time-consuming and requires domain expertise.
Core Logic
Leverages Claude 3.5 Sonnet (temperature 0.7) to generate retailer-specific campaign briefs, executive summaries, and presentation materials tailored to each retail partner.
Worker Overview
Technical specifications, architecture, and interface preview
System Overview
Technical documentation
Tech Stack
8 technologies
Architecture Diagram
System flow visualization