ProSearchAI
ProSearchAI: Making product searches faster and easier with real-time AI that gives personalized results and helps users find the best products quickly.
Created on 15th September 2024
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ProSearchAI
ProSearchAI: Making product searches faster and easier with real-time AI that gives personalized results and helps users find the best products quickly.
The problem ProSearchAI solves
ProSearchAI solves the problem of slow and inefficient product searches. Many search engines give too many irrelevant results, making it hard for users to find exactly what they need. Traditional methods often waste time, requiring users to filter through countless options or perform multiple searches to compare products.
Use Cases:
People can use ProSearchAI for:
1). Faster Product Searches: It helps users find products quickly by providing accurate results tailored to their needs.
2). Personalized Product Comparisons: Users can compare products based on specific queries, making it easier to choose the best option.
3). Smarter Recommendations: The system uses AI to give suggestions that match what users are really looking for, making shopping decisions easier.
How It Improves Existing Tasks:
1). Saves Time: By reducing the number of irrelevant search results, users can find what they need without wasting time.
2). Makes Comparison Easier: Users get instant comparisons of products, cutting down the effort required to make informed decisions.
3). Better Accuracy: It uses advanced AI technology, which means the search results are more accurate and relevant to what the user wants.
4). Enhanced User Experience: The tool provides a smoother and more intuitive shopping experience by giving users the information they need quickly and easily.
Challenges we ran into
Challenges We Ran Into:
One of the main challenges we faced while building ProSearchAI was ensuring data accuracy and integration from various sources. Handling large volumes of product data and providing accurate results was tricky, especially when trying to maintain speed and efficiency.
Another hurdle was optimizing the retrieval-augmented generation (RAG) pipeline for real-time comparisons. We encountered issues where the system returned irrelevant results due to inconsistencies in data retrieval.
To overcome this, we improved the data preprocessing steps by:
Implementing a more rigorous data cleaning process to filter out noisy data.
Using cosine similarity to enhance the accuracy of query responses and improve the matching of products with user queries.
Additionally, balancing system resource costs while maintaining high performance was difficult, especially during testing. We mitigated this by optimizing the data storage and retrieval processes and leveraging scalable cloud infrastructure.