EcoStep – Carbon Footprint from Receipts, Made Easy

EcoStep helps users track carbon footprints from receipts, chat with an eco-AI, and create monthly CO₂ reports using AI and cloud tech—all in one platform.

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Inspiration

Every purchase we make has a hidden cost — not just financial, but environmental. While working on sustainability-themed hackathons, we noticed a surprising gap: there were tools for tracking finances, calories, even screen time — but hardly anything for tracking your carbon footprint from everyday shopping. That got us thinking: what if a simple photo of a receipt could help people understand their impact on the planet?

That thought sparked EcoStep — a playful but powerful tool that turns boring receipts into meaningful climate feedback. We were especially inspired by the rise of LLMs and how they can interpret unstructured data in ways traditional pipelines never could. With Groq’s speed and precision, we saw the opportunity to make carbon tracking accessible, engaging, and even a little fun.

What it does

EcoStep lets users upload shopping receipts, then uses a Groq-powered LLM to:

  • Extract item details (name, quantity, category)
  • Estimate carbon footprint for each item
  • Store entries in a searchable database
  • Generate monthly reports with visual charts and AI-generated sustainability suggestions
  • Answer user questions about their footprint via a friendly AI chatbot
  • Send results via WhatsApp and store reports in Google Drive

In short: it turns paper clutter into environmental clarity — automatically.

How we built it

We built EcoStep using Python and Streamlit for the interface. Here’s how the key components work:

  • OCR & Parsing: Users upload a receipt image. We preprocess it and then prompt a Groq-hosted LLM to extract structured JSON from the raw text (item, category, quantity, and CO₂).
  • Database: Parsed data is validated and stored in a local SQLite database.
  • Reporting Engine: A custom script summarizes daily and monthly carbon data using Matplotlib to create visual reports.
  • Groq LLM Reports: We generate sustainability suggestions for each user based on their activity via Groq’s LLM, making reports personalized and useful.
  • Chatbot: Users can chat in natural language with an LLM to explore their footprint. Questions like “What’s my biggest carbon source?” are translated into SQL under the hood.
  • Integrations: We use Twilio API to send WhatsApp messages and Google Drive API to auto-upload the reports.

Challenges we ran into

  • Unstructured Receipts: Receipts are messy. Traditional OCR was inconsistent, so we had to fine-tune how we structured prompts to Groq to get reliable outputs.
  • Data Validation: Often, LLMs returned partial or inconsistent data. We had to build fallback logic and sanitization layers.
  • SQL Generation: Building the chatbot to translate vague user queries into correct SQL queries was tricky. We had to test many edge cases and implement a retry mechanism.
  • Report Personalization: Making report suggestions feel relevant required clever prompt engineering and a good understanding of prompt chaining.
  • API Quotas & Secrets: Managing authentication for Twilio and Google Drive, especially with Streamlit's deployment constraints, took some work.

Accomplishments that we're proud of

  • Built a full working pipeline from receipt image → carbon footprint estimate → chatbot + reports
  • Leveraged Groq’s LLMs in 3 distinct use cases (data extraction, Q&A, and report suggestions)
  • Created a real-time, intelligent sustainability assistant that works with just a photo
  • Designed a user-friendly app that turns guilt into guidance, and data into action
  • Shipped something meaningful that blends AI, climate action, and simplicity

What we learned

  • Prompt engineering is a skill in itself — especially when you want consistent, structured outputs from LLMs
  • Groq’s LLMs are not only fast, but versatile — from parsing to reasoning to generating content
  • Sustainability tech needs to be easy, not preachy — the best tools blend into people’s lives
  • Building an AI product is not just about the model, but also about UX, fail-safes, and trust

What's next for EcoStep – Carbon Footprint from Receipts, Made Easy

  • Real-time dashboard: Let users monitor and compare their footprint over time
  • More accurate emissions data: Integrate a verified carbon dataset (e.g., from GHG Protocol or Climatiq API)
  • Group reports: Help families or teams track collective impact
  • Gamification & badges: Reward eco-friendly habits
  • Public leaderboard: Let users optionally compare progress with friends
  • Mobile app: Take it out of the browser and into everyone’s pockets

We believe EcoStep has the potential to become the Fitbit for your carbon footprint — and we’re just getting started. 🌍📉

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