Play 89
Retail Inventory Predictor
High✅ Ready
Demand forecasting with automated supplier reordering.
Demand forecasting and inventory optimization combining historical sales data, weather patterns, social media trends, and economic indicators to predict stock needs and automate supplier reordering. OpenAI analyzes trend signals and generates insights, Machine Learning builds forecasting models, Cosmos DB stores inventory and sales history, Event Hubs ingests real-time POS data, and Functions orchestrate reorder workflows.
Architecture Pattern
Inventory prediction pipeline: sales data ingestion - trend analysis - demand forecasting - safety stock calculation - reorder automation
Azure Services
Azure OpenAIAzure Machine LearningAzure Cosmos DBAzure Event HubsAzure Functions
DevKit (.github Agentic OS)
- agent.md — root orchestrator with builder→reviewer→tuner handoffs
- 3 agents — Inventory Builder (gpt-4o), Reviewer (gpt-4o-mini), Tuner (gpt-4o-mini)
- 3 skills — deploy (208 lines), evaluate (112 lines), tune (233 lines)
- 4 prompts — /deploy, /test, /review, /evaluate with agent routing
- .vscode/mcp.json — FrootAI MCP with OpenAI key input + envFile
TuneKit (AI Config)
- config/openai.json - trend analysis and insight generation prompts
- config/inventory.json - forecast horizons, safety stock, supplier lead times
- config/guardrails.json - forecast accuracy thresholds, overstock limits
- evaluation/eval.py - Forecast accuracy >85%, Stockout reduction >30%
Tuning Parameters
Forecast horizon daysSafety stock multiplierReorder point formulaSupplier lead time configTrend signal weights
Estimated Cost
Dev/Test
$80-200/mo
Production
$2K-8K/mo