ConsumeWise
Embrace the Wisdom of Conusmption.
Created on 1st October 2024
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ConsumeWise
Embrace the Wisdom of Conusmption.
Describe your project
ConsumeWise is a Gen AI-powered public information system that enhances transparency and trust in consumer goods like food and personal care products. It provides personalized, verifiable insights on health, environmental, and societal impacts, empowering consumers to make informed, sustainable choices.
In-scope features include data collection from multiple sources, unbiased verification, and AI-driven personalization to tailor recommendations based on user preferences. The system integrates seamlessly with e-commerce platforms, offering real-time insights.
Out-of-scope areas include product pricing, full supply chain audits, predictive health diagnostics, and regulatory enforcement.
Future opportunities include global expansion, blockchain integration, expansion into new product categories, and partnerships with governments and NGOs to drive sustainability and ethical consumerism. ConsumeWise aims to foster conscious consumption by making data accessible and actionable during shopping experiences.
Challenges we ran into
One of the main challenges we faced while building ConsumeWise was ensuring data accuracy and credibility. Collecting publicly available data from different sources, such as producers, third-party verifiers, and open databases, introduced inconsistencies in the information quality. Verifying the accuracy of this data in a decentralized, unbiased manner was complex, as many sources had conflicting standards for health and sustainability.
To address this, we implemented a multi-layered data validation process. First, we integrated third-party verifiers with proven credibility in health, environmental, and ethical certifications. Then, we used AI algorithms to cross-reference information from multiple sources, flagging inconsistencies and assigning reliability scores to each dataset. This helped us prioritize trusted sources and improve overall data quality.
Another issue involved optimizing real-time AI-driven recommendations without causing cognitive overload for users. Initially, the system provided too much information, overwhelming users during their shopping experience. We solved this by simplifying the insights into clear, actionable scores and nudges that align with users' preferences.
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