OptimizeAI

Home Screen
Energy Saving
fish-tank
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Inspiration

With the rise of artificial intelligence, the cost of each query in an LLM and the inefficient energy handling have led us to consider if there is a better way to handle these queries. Our goal was to create a more sustainable alternative—an intelligent system that parses user queries and converts them into optimized queries using alternative methods. By doing so, we reduce unnecessary AI processing while still providing users with relevant information.

What it does

How we built it

Our web app consists of three main components:

Query Parser & Search Optimizer We designed an NLP-based system to analyze user queries and extract key terms. The extracted keywords are used to generate refined Google searches, reducing the need for LLM processing.
Energy-Saving Dashboard Tracks how much energy users save by using our app instead of querying an LLM. Provides insights into the environmental impact of AI usage.
Gamification: Virtual Fish Tank Users earn tokens based on energy saved. Tokens can be used to purchase virtual fish that swim in a digital aquarium.

Challenges we ran into

Efficiency vs. User Experience: Maintaining a seamless search experience while minimizing AI resource consumption. Implementing Accurate Subquery Breakdown: Shortcutting LLM usage with a more energy-efficient way requires subquery interpretation

Accomplishments that we're proud of

Significantly reduces energy cost for LLM queries Designed an engaging energy-tracking dashboard that visualizes the impact of reduced LLM usage.

What we learned

Natural Language Processing (NLP): Optimizing query parsing for better search results. Energy Efficiency in Computing: Understanding the environmental impact of AI and strategies to reduce it. Gamification & User Retention: Designing interactive features to encourage sustainable user behavior.