AI Product Launch Assistant
Deploys a 6-agent orchestrated AI system that analyzes retailer profiles, calculates multi-dimensional product-retailer fit scores across 5 dimensions, predicts authorization probability using Gradient Boosting ML trained on 1,247 historical outcomes with 47 features, gathers competitive intelligence, and generates customized 12-slide pitch decks for priority retailers..
Problem Statement
The challenge addressed
Solution Architecture
AI orchestration approach
Product Information Input - Quick-start templates and product details form for plant-based protein launch workflow
Multi-Agent Analysis - Real-time 6-agent collaboration analyzing 425 retailers for optimal product-retailer fit scoring
AI Analysis Complete - Executive summary with 425 retailers analyzed, 15 priority targets, and 68% authorization probability
Market Intelligence - Competitive landscape analysis with trending attributes, consumer insights, and market gap identification
AI Agents
Specialized autonomous agents working in coordination
Launch Orchestrator
Product launch workflows require careful coordination across multiple analysis stages to synthesize coherent recommendations.
Core Logic
Uses Claude 3.5 Sonnet (temperature 0.2) to coordinate workflow execution, manage agent handoffs, and synthesize final launch recommendations from multiple analytical inputs.
Retailer Intelligence Agent
Understanding the nuances of 425+ retailers including buyer preferences, category strategies, and authorization requirements is overwhelming.
Core Logic
Leverages GPT-4 Turbo (temperature 0.3) to analyze retailer profiles, identify buyer preferences, map category strategies, and surface relevant authorization requirements for target retailers.
Product-Retailer Fit Analyzer
Determining product-retailer compatibility requires multi-dimensional analysis across pricing, positioning, demographics, and category fit.
Core Logic
Applies Claude 3.5 Sonnet (temperature 0.2) to calculate compatibility scores across 5 dimensions, identifying optimal retailer matches based on product attributes and retailer characteristics.
Authorization Predictor
Predicting authorization success requires analyzing complex patterns across historical outcomes, retailer behaviors, and product attributes.
Core Logic
Employs custom Gradient Boosting ML model trained on 1,247 historical authorization outcomes with 47 predictive features, combined with GPT-4 (temperature 0.1) for contextual interpretation.
Competitive Intelligence Agent
Understanding competitive landscape and identifying differentiation opportunities is critical for successful retail pitches.
Core Logic
Utilizes GPT-4 Turbo (temperature 0.3) to research competitive products, identify market gaps, and surface differentiation points for compelling buyer presentations.
Presentation Generator
Creating customized, compelling pitch presentations for each retailer is time-intensive and requires deep understanding of buyer priorities.
Core Logic
Leverages Claude 3.5 Sonnet (temperature 0.7) to generate customized 12-slide pitch decks tailored to each retailer's priorities, category strategy, and buyer preferences.
Worker Overview
Technical specifications, architecture, and interface preview
System Overview
Technical documentation
Tech Stack
6 technologies
Architecture Diagram
System flow visualization