Enterprise Decision Intelligence Platform (EDIP)

Retail and distribution teams need grounded demand forecasts and inventory decision support that can combine quantitative signals with explainable business reasoning.

The system joins forecasting, retrieval, agent coordination, recommendation logic, business-facing APIs, a web frontend, event simulation, orchestration, testing, and observability in one portfolio architecture.

Specialized agent roles coordinate retrieval, forecasting context, recommendation preparation, and explainable response generation through a controlled workflow.

Retrieval-Augmented Generation uses OpenAI models with Pinecone-backed context to ground business reasoning in retrieved information rather than unsupported free-form generation.

XGBoost forecasting feeds replenishment recommendation logic designed to support inventory decisions. No unsupported accuracy or business-impact metrics are claimed.

A FastAPI backend exposes business-facing interfaces, while a React and Next.js frontend provides the application surface using TypeScript.

Kafka supports event-driven simulation and Airflow represents scheduled orchestration across the decision workflow.

Unit and integration testing provide evidence for individual components and the validated local end-to-end workflow.

Prometheus and Grafana form the monitoring surface for system behaviour, service signals, and operational inspection.

Docker packaging, Kubernetes manifests, Terraform, and GitHub Actions demonstrate deployment-oriented infrastructure without claiming a live production deployment.

The validated evidence is local and portfolio-oriented. Production scale, users, savings, live deployment, and model-performance outcomes are not claimed.

Potential next steps include stronger evaluation evidence, broader real-data validation, security hardening, controlled cloud trials, and deeper operational testing.