Co-founder
Yaysay
10/10Traction
AI-powered shopping app that personalizes product recommendations by analyzing user preferences and behavior. It streamlines the shopping experience, helping users discover relevant items quickly while reducing decision fatigue.
- Artificial Intelligence
- Applications
- Commerce & Shopping
- Retail Technology
- Software Only
Updated 22 months ago
Funding
Raised to date
$10.3MRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
A*No round attributed
- a-star.co (opens in a new tab)investor website
Afore CapitalNo round attributed
- afore.vc (opens in a new tab)investor website
Habitat PartnersNo round attributed
- habitatpartners.co (opens in a new tab)investor website
Lightspeed Venture PartnersNo round attributed
- Source unavailable
Funding rounds are not available yet.
Founders
Product
Problem
Online shoppers often face information overload and decision fatigue due to the vast selection of products and lack of personalized recommendations. Sifting through irrelevant items and generic suggestions can be time-consuming and frustrating, leading to abandoned carts and missed sales opportunities for retailers.
Solution
This startup offers an AI-driven shopping application that learns individual user preferences and behaviors to deliver highly relevant product recommendations. By analyzing browsing history, purchase patterns, and demographic data, the app filters out irrelevant items and presents a curated selection of products tailored to each shopper's unique needs and tastes. This personalized approach streamlines the shopping experience, reduces decision fatigue, and increases the likelihood of purchase.
Target Audience
The primary target audience is online retailers seeking to improve customer engagement and conversion rates, as well as individual shoppers looking for a more personalized and efficient shopping experience.
Features
- AI-powered recommendation engine that analyzes user behavior and preferences
- Personalized product feeds based on individual shopping history and browsing patterns
- Smart filtering and sorting options to quickly narrow down product choices
- Real-time adaptation to changing user preferences and market trends