FaceFrame provides an AI‑driven eyewear shopping experience that uses machine learning and facial recognition to scan a user’s face and generate personalized frame recommendations. The platform offers an AR virtual try‑on tool and lets shoppers filter by brand, color, price, and style, while in‑store kiosks enable quick face scans for seamless, accurate fitting without manual measurements.
Funding
Funding not disclosed
Founders
Product
Problem
Consumers often struggle to find eyeglass frames that both fit their facial dimensions and match their personal style, leading to time‑consuming trial‑and‑error in stores or uncertainty when shopping online.
Solution
FaceFrame applies machine‑learning–driven facial recognition to analyze a user’s face shape within seconds. The system generates a curated list of frame recommendations that align with the individual’s dimensions and style preferences. Users can preview each suggestion through an augmented‑reality virtual try‑on, either on the website or at partner kiosks equipped with on‑site scanning stations. This streamlined workflow eliminates manual measurements and reduces the need for repeated in‑store visits, delivering a personalized eyewear selection experience from start to purchase.
Target Audience
Primary customers are individual eyeglass shoppers seeking a fast, accurate fit, as well as optical retailers and eyewear brands that want to enhance their sales channels with AI‑driven personalization.
Features
- AI-powered face scan that extracts facial geometry to determine optimal frame shapes and sizes
- Curated recommendation engine that filters frames by brand, color, price, and style preferences
- AR virtual try‑on allowing real‑time visualization of frames on the user’s face
- Integrated online and in‑store kiosk experience for seamless face scanning and immediate results
- Quick recommendation delivery within seconds, reducing shopping time and guesswork