Alagbero is a platform that helps users build and continuously update a personal style profile, delivering AI‑driven outfit recommendations tailored to their evolving tastes. By analyzing photos of a user’s wardrobe and past choices, the service suggests coordinated looks and new pieces that match their preferences, streamlining everyday fashion decisions.
Funding
Funding not disclosed
Founders
Product
Problem
Consumers often struggle to keep track of their personal fashion preferences and to find clothing and accessories that align with their evolving style, leading to inefficient shopping and missed outfit opportunities.
Solution
Alagbero offers a web-based platform where users create a dynamic style profile that updates as they input preferences, likes, and past purchases. The platform uses algorithmic recommendations to suggest outfits and accessories tailored to the user's current taste. By centralizing style data, Alagbero simplifies outfit planning and streamlines the decision‑making process for online shopping, helping users discover items that fit their personal aesthetic without extensive browsing.
Target Audience
Alagbero targets fashion‑savvy consumers who shop online and want a personalized, continuously updated style guide to aid outfit selection and purchasing decisions.
Features
- Interactive style questionnaire and ongoing preference inputs to continuously refine the user profile
- Algorithm-driven product recommendations that adapt to changes in the user's fashion tastes
- Integrated outfit planning tool that allows users to curate and save look combinations
- Direct links to partner retailers for seamless purchase of recommended items
- Personal style analytics dashboard showing trend evolution and favorite categories