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🌱 EcoPilot Agent — Your AI-Powered Carbon Reduction Companion

Inspiration

Climate change is one of the biggest global challenges, yet most people still don't understand how their daily lifestyle impacts the environment. Existing carbon footprint tools only provide static reports or generic tips, leaving users without meaningful action or long-term engagement.

We wanted to build something beyond a traditional chatbot.

Our vision was to create an autonomous AI agent that not only analyzes emissions, but also actively helps users reduce them through intelligent planning, personalized recommendations, and continuous progress tracking.

That idea became EcoPilot Agent — an AI-powered sustainability assistant designed to transform awareness into measurable environmental action.

What it does

EcoPilot Agent helps users:

Track and analyze their carbon footprint
Store sustainability data securely
Generate personalized AI-driven reduction plans
Monitor environmental progress dynamically
Receive adaptive recommendations based on habits
Visualize carbon emissions through dashboards and analytics

Unlike static calculators, EcoPilot behaves like an intelligent agent: it understands user data, reasons about sustainability patterns, generates actionable roadmaps, and continuously guides users toward greener behavior.

How we built it

We built EcoPilot using a modern full-stack architecture focused on scalability, intelligent workflows, and real-world usability.

Frontend

Next.js App Router
React 19
Tailwind CSS
shadcn/ui
Recharts for analytics visualization

Backend & Database

Node.js server APIs
MongoDB Atlas
Mongoose ODM

AI Layer

Google Gemini AI (@google/genai)
AI-powered analysis and roadmap generation
Intelligent sustainability recommendations

Core System Workflow

Users submit lifestyle and carbon-related activity data
The backend processes and stores logs in MongoDB
Gemini AI analyzes emission patterns
EcoPilot generates a personalized 4-week sustainability roadmap
Dashboards visualize environmental impact and progress over time
The agent dynamically adapts recommendations as user behavior changes

Challenges we ran into

One of the biggest challenges was designing EcoPilot as a true agentic system rather than a simple AI chatbot.

We had to carefully architect:

multi-step AI workflows,
structured data pipelines,
personalized recommendation generation,
dynamic state management,
and secure cloud-based storage.

Integrating AI-generated sustainability plans with real-time user tracking required balancing:

accuracy,
scalability,
and responsiveness.

Another challenge was creating an interface that felt modern and intuitive while still communicating complex environmental data clearly.

Accomplishments that we're proud of

Built a fully functional AI-powered sustainability platform
Successfully integrated Gemini AI for intelligent carbon analysis
Created adaptive sustainability roadmaps instead of static suggestions
Developed dynamic dashboards for impact visualization
Designed a premium climate-tech inspired UI/UX
Implemented secure cloud-based data storage using MongoDB Atlas
Created a scalable architecture for future AI agent expansion

What we learned

During this hackathon, we learned:

how to build autonomous AI-driven workflows,
how modern AI agents differ from traditional chat systems,
how to combine AI reasoning with structured databases,
and how impactful sustainability-focused technology can be when paired with intelligent automation.

We also gained deeper experience with: