EcoLoop_AI
Discover how EcoLoop AI combines artificial intelligence and AWS cloud technologies to make waste segregation, collection, and recycling more connected and transparent. Explore our approach to building a scalable, technology-driven solution that supports smarter resource recovery and a more sustainable circular economy.
Ai Engineer
The Day I Started Looking at My Dustbin Differently
Picture a normal evening in an Indian kitchen. Dinner is done. A banana peel, an empty milk packet, a torn newspaper and a dead phone charger all go into the same bin. Nobody feels guilty, because it's what everyone does.
Then, early next morning, someone arrives to carry that bin away. They are usually the one person in the whole chain who never gets thanked. They will sort through our mess with their bare hands, because we never sorted it ourselves.
That image is where EcoLoop AI began.
We don't throw things away because we don't care
Most of us do care. We just never learned the rules. Is a greasy pizza box recyclable? What about a toothpaste tube? Where does an old charger go? Nobody ever told us, and the system around us doesn't make it easy to find out.
So everything gets mixed, and mixing destroys value. Dry paper gets soaked by wet waste. Plastic gets dirty. Material that could have lived a second life ends up in a landfill.
The pain doesn't stop at the household:
- Collectors do some of the hardest work in the city with almost no digital tools. Their routes are informal, and their effort goes unrecorded.
- Recyclers struggle to find clean, sorted material in the quantities they need, while tonnes of it never reach them.
- Cities and housing societies are expected to report on waste, but without real data they are working half-blind.
Waste isn't only a disposal problem. It is a visibility problem. Nobody can see where it goes.
So we asked a simple question
What if waste management could be smarter, more connected and more honest?
EcoLoop AI is the answer we are building: a platform that follows waste from your doorstep to verified recycling. It is built around one belief:
Waste is a resource out of place. Identify it. Collect it. Route it. Recover it.
How EcoLoop AI works
1. Know what you're holding. Take a photo of any item and the app suggests what it is and where it belongs. If the AI isn't sure, it says so and asks for a human check. We would rather admit doubt than give you a confident wrong answer.
2. Get it picked up without the hassle. Request a pickup with the waste type, rough quantity, address and time. Collectors see their jobs, update the status, and every request becomes a tracked task.
3. Match waste to the people who value it. Recyclers and scrap dealers can find material that fits what they accept, by type, quantity, location and quality.
4. Follow the journey. Every batch moves through clear stages: Requested → Assigned → Collected → Dispatched → Received → Processed. Each stage is meant to be backed by a record.
That last part matters most to us. Collecting waste is not the same as recycling it. Anyone can say "it was recycled." We want people to be able to see it for themselves.
What's under the hood (built on AWS)
We chose a serverless architecture so a small student team can build something that could grow.
- AWS Lambda runs our Python business logic.
- Amazon API Gateway is the secure front door for our web and mobile apps.
- Amazon DynamoDB stores application data and material-tracking records.
- Amazon S3 holds waste images and supporting evidence.
- Amazon Cognito handles sign-up and sign-in.
- Amazon Rekognition Custom Labels is our planned model for recognising waste categories.
- Amazon CloudWatch helps us monitor logs and catch errors.
- AWS SAM defines and deploys the whole stack as code.
The front end uses React and TypeScript for web, and React Native with Expo for mobile.
What we refuse to pretend
An honest project should say what it can't do yet, so here it is:
- AI can be wrong. A predicted "recyclable" item may still be contaminated or rejected by a facility. Real-world verification always comes first.
- A good classifier needs good, accurately labelled local data, and recycling rules differ from place to place.
- Impact numbers should separate estimated from verified. We don't want a dashboard that flatters us.
- People's addresses and personal data must be protected.
Technology alone won't fix waste. It also takes habits, collectors, recycling infrastructure and communities who care.
The people we're building this for
For the mother who wants to do the right thing but doesn't know how.
For the collector whose work deserves to be seen and recorded.
For the recycler who needs good material, and for the city that needs numbers it can trust.
If we do this right, doing the right thing becomes easier than doing the wrong thing.
Where we want to go next
Multilingual guidance for Indian communities, QR-based handover records, offline-friendly tools for collectors, route optimisation, verified reward points, and a classifier trained on local data. We'll add these step by step, putting reliability ahead of complexity.
One last thought
We believe waste isn't really waste until we stop paying attention to it. Paper, plastic, metal and glass all keep their value, and they lose it only when we mix them up and look away.
EcoLoop AI is our way of looking closer.
Identify it. Collect it. Route it. Recover it.
Built with a sustainability-first mindset by Team DevX.
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