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Solve Team

Thank you for responding to the comments. Please note that judges won’t read replies to comments. If you haven’t already, be sure to add this information to your application before the deadline on July 22nd at 9am ET!

Solve Team

Thank you for responding to this comment. Please note that judges won’t read replies to comments. If you haven’t already, be sure to add this information to your application before the deadline on July 22nd at 9am ET!

Solve Team

In response to If you would like to apply for the GM Prize on Circular Economy, describe how you and your team will utilize the prize to advance your solution.

Your solution looks like it might be eligible for the GM Prize. If you haven't already, check out the eligibility requirements and consider applying by completing the prize-specific question in the application.

Solve Team

In response to Describe the core technology that your solution utilizes.

Can you explain more specifically how you are using both machine learning and AI in your solution?

Stephanie Benedetto

Whether a business is discovering an item of dead stock to buy, listing waste to sell, or communicating with a buyer or seller, AI is behind the scenes making the experience better and more efficient.

The quicker we can match a buyer with a seller, the quicker we can keep this waste out of landfill!

Here's how Queen of Raw implements AI:

1. Product Recommendations (Machine Learning): We collect information based on actions our community takes and make recommendations and offer substitutions for dead stock products based on past behaviors and the prices we are seeing trend in the secondary market for waste

2. Chatbots (Natural Language Processing): We have chatbots for customer support and provide automated answering of questions based on the text provided by our community to help them understand our business and our mission--for people, for planet, and for profit

3. Analyzing Customer Images (Computer Vision): We use image recognition to capture and analyze information like the image objects, colors, and characteristics from images of waste uploaded by our community and are building these into our recommendation processes

4. Search Functionality (Natural Language Processing + Machine Learning): We have algorithms for our search functionality to manipulate search results, autocomplete the search, and do synonyms and product suggestions for misspelled words

As we collect this data around waste, we can start to immediately identify waste in a supply chain, automate the on-boarding process to our marketplace, and then help our customers make intelligent predictions to minimize those waste streams going forward.

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