Sustainability Intelligence Hub

Sustainability Intelligence Hub: Predict, Prevent, Optimize.
Executive Landing Page: Global Risk Intelligence at Scale.
Command Center: Real-time ESG KPIs and Trend Analysis.
Risk Heatmap & Simulation Panel powered by Databricks.
AI Consultant: Context-aware recommendations via Gemini 1.5 Pro.

Inspiration

Global supply chains face unprecedented threats from climate change and geopolitical instability. We realized that enterprise executives lack real-time, actionable intelligence to mitigate these ESG (Environmental, Social, and Governance) risks proactively. We wanted to bridge the gap between raw sustainability data and strategic decision-making.

What it does

SustainabilityHub is an enterprise-grade command center that provides:

Live Risk Exposure: Continuous modeling of environmental and logistics disruptions across global regions.
Executive Simulation: An AI-driven scenario planning engine to test supply chain resilience against specific threats (e.g., SE Asia Climate risks).
AI Consultant RAG: A Digital ESG Agent that offers strategic, context-aware recommendations (like route diversification) to executives.

How we built it

Frontend User Interface: We utilized Next.js (React) with Tailwind CSS and Framer Motion to build a responsive, highly interactive executive dashboard. Lucide React was used for standardized iconography.
Data Architecture: We leveraged the Databricks Lakehouse architecture, implementing a Medallion pipeline (Bronze to Gold tables) to process raw ingestion into business-ready insights.
AI Integration: We integrated Google Gemini 1.5 Pro utilizing a RAG (Retrieval-Augmented Generation) framework to power our interactive Chat Panel and Executive Simulation scenarios.
Authentication & Deployment: Secured using NextAuth/Firebase and deployed on Vercel for edge performance.

Challenges we ran into

Building a unified interface that handles complex, high-volume simulated data while maintaining sub-second latency for the AI Consultant queries was significantly challenging. We had to heavily optimize our Next.js API routes (/api/simulate, /api/metrics) to manage Databricks SQL Warehouse state transitions seamlessly.

What we learned

We significantly deepened our expertise in Databricks Delta Lake architectures and streaming data pipelines. Additionally, we mastered advanced prompt engineering and RAG patterns with Gemini 1.5 Pro to ensure our AI agent delivers strictly professional, enterprise-level business intelligence rather than generic responses.