## Inspiration

Sustainability is no longer a nice-to-have, it’s essential for future-proofing investments and businesses. Investors struggle to find companies that align with their sustainability goals, while companies face challenges identifying best practices in environmental responsibility. Our team saw the need for a solution that helps both investors and businesses thrive in the sustainable economy.

## What it does

Our platform helps investors and companies alike by:

* **Empowering Investors:** Find investment opportunities in companies with strong sustainability practices using our advanced semantic search.
* **Predictive Risk Modeling:** Provides future environmental risk scores for companies, helping investors build eco-friendly portfolios.
* **Portfolio Optimization:** Investors receive personalized recommendations on how to improve their sustainability scores by diversifying into companies with growing eco-friendly initiatives.
* **Research Tool for Companies:** Businesses can explore the sustainability strategies of industry leaders, using our platform to research and improve their own practices.

## How we built it

We utilized cutting-edge technology to bring this vision to life. For the semantic search, we used a vector database (SingleStore) and cosine similarity to allow investors to search for companies based on specific sustainability traits. We integrated Hugging Face models to generate embeddings that power the semantic search. On the data analysis side, we used NumPy and scikit-learn to implement linear regression, predicting future environmental risk scores for companies. The environmental risk predictions and comparisons with the S&P 500 are visualized through Chart.js, giving users clear and insightful data representations.

## Challenges we ran into

Building an app with this level of complexity came with its challenges. Implementing accurate semantic search required careful tuning of the embeddings and vector search algorithms. Collecting and standardizing environmental data across a large number of companies was also a big hurdle. Additionally, fitting linear regression models to predict future risk scores based on historical data required rigorous testing and validation to ensure accuracy.

## Accomplishments that we're proud of

We are proud of the seamless integration of advanced technologies like semantic search and predictive analytics. Our vector search engine allows investors and sustainability teams to find relevant companies with incredible ease. Our linear regression models give forward-looking insights, helping investors and companies make data-driven decisions to improve sustainability. Most importantly, the potential for this tool to drive real-world environmental impact makes this project truly rewarding.

## What we learned

Throughout this project, we learned the power of combining machine learning with sustainable business practices. We deepened our understanding of semantic search algorithms, vector databases, and regression modeling for predictive analytics. We also realized how crucial well-organized data is to create meaningful predictions and insights. Building a product that bridges sustainability with investment strategy was complex, but it gave us experience in balancing technical precision with user-friendly design.

## What's next for Envest

Our next step is to expand the platform by incorporating more dynamic data sources, including real-time environmental news and company sustainability reports. We want to broaden our recommendation system, providing even more refined suggestions for portfolio improvement. On the company side, we plan to introduce tools that allow businesses to simulate the impact of implementing specific sustainability practices, helping them visualize potential gains in environmental scores.
## Inspiration

I came across a quote that deeply moved me: 'By 2050, a further 24 million children are projected to be undernourished as a result of the climate crisis.' It made me reflect profoundly on the significant impact climate change has on our world."
Melting ice caps and glaciers contribute to rising sea levels and warmer temperatures, threatening the availability of land for future generations. This inspired us to create an application that educates individuals about their carbon footprint, suggests ways to reduce it, and leverages AI technology to assist in these efforts, thereby promising a sustainable future for everyone.

## What it does

The app provides users with an overview of their carbon footprint based on their daily activities. It provides an intuitive textual response from which users can gain actionable insights.

Key features include:

* **Home Page**: Displaying a summary of the user's overall carbon footprint, along with actionable advice on reducing it.
* **Interactive Chatbot**: Users can engage in voice or text conversations with an AI-driven chatbot to gain a deeper understanding of their carbon output and receive personalized suggestions.
* **Receipt Analysis**: Users can upload images of their receipts, and the app utilizes AI to extract data and calculate the associated carbon footprint.
* **Transport Tracker**: Users input their mode of transportation along with the travel duration and distance, and the app calculates the carbon emissions for these activities.
* Users can review detailed reports and insights to identify high-emission areas and explore ways to reduce their footprint without significantly altering their lifestyle.

## How we built it

We utilized a blend of modern web technologies and advanced AI services:

* **Frontend**: Developed using HTML, CSS, and JavaScript to ensure a responsive and user-friendly interface.
* **Backend**: Implemented with Flask, which facilitates communication between the frontend and various AI models hosted on Hugging Face and other platforms.
* **AI Integration**: We integrated Amazon Bedrock for inference with LLMs like Anthropic Claude 3, OpenAI's Whisper for speech-to-text capabilities, and Moondream2 for image-to-text conversions.
* **Data Storage**: Utilized AWS S3 buckets for robust and scalable storage of images, audio files, and textual data.

## Challenges we ran into

* One significant challenge was the integration of the Whisper model for transcription, primarily due to compatibility issues with different versions of OpenAI's API. This required extensive testing and modifications to ensure stable functionality.
* Building the front end was a little complex since we mainly focused on backend and machine learning models for projects.

## Accomplishments that we're proud of

We are particularly proud of developing a tool that can significantly impact individuals' ecological footprints. By providing easy-to-understand data and actionable insights, we empower users to make informed decisions that contribute to global sustainability.

## What we learned

This project enhanced our skills in collaborative problem-solving and integrating multiple technologies into a seamless application. We gained deeper insights into the capabilities and practical applications of large language models and AI in environmental conservation.

## What's next for the project?

Looking ahead, we plan to:

* **Expand Device Integration**: Enable syncing with smartphones and wearable devices to track fitness and travel data automatically.
* **Financial Integration**: Connect with banking apps to analyze spending patterns and suggest more eco-friendly purchasing options.
* **Community Challenges**: Introduce features that allow users to participate in challenges with friends or colleagues, fostering a competitive spirit to achieve the lowest carbon footprint.

Our goal is to continually evolve the app to include more features that will not only help individuals reduce their carbon footprint but also engage communities in collective environmental responsibility.
## Inspiration

We admired the convenience Honey provides for finding coupon codes. We wanted to apply the same concept except towards making more sustainable purchases online.

## What it does

Recommends sustainable and local business alternatives when shopping online.

## How we built it

Front-end was built with React.js and Bootstrap. The back-end was built with Python, Flask and CockroachDB.

## Challenges we ran into

Difficulties setting up the environment across the team, especially with cross-platform development in the back-end. Extracting the current URL from a webpage was also challenging.

## Accomplishments that we're proud of

Creating a working product!
Successful end-to-end data pipeline.

## What we learned

We learned how to implement a Chrome Extension. Also learned how to deploy to Heroku, and set up/use a database in CockroachDB.

## What's next for Conscious Consumer

First, it's important to expand to make it easier to add local businesses. We want to continue improving the relational algorithm that takes an item on a website, and relates it to a similar local business in the user's area. Finally, we want to replace the ESG rating scraping with a corporate account with rating agencies so we can query ESG data easier.