Play 89
Retail Inventory Predictor
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
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
Machine evidence
FrootAI evidence lifecycle
This is an internal evidence maturity label, not third-party certification, accreditation, legal compliance, or a production guarantee. Missing or expired evidence demotes automatically; catalog claims cannot promote a play.
This play currently has design evidence only. A runnable scenario, endpoint evaluation, and build receipts are the next contiguous gates.
Repo Intelligence
v1A no-clone, revision-pinned map for agents and humans. Observed evidence is separated from inferred flow so the output stays useful without pretending to be a full call graph.