GREYTT PLATFORM
AI-Driven Lifestyle Platform for the 50+ Demographic
GREYTT SCORE
GREYTT SCORE
Introduction
Greytt is a personalization platform built by HighOnSwift for users aged 50+, focused on travel, wellness, and social engagement. It uses behavioral and preference data to drive AI-generated recommendations, with an interface designed around reduced cognitive load and accessibility constraints common in older-adult UX (larger touch targets, simplified navigation depth, high- contrast typography).
The Challenge
•Existing lifestyle/travel platforms are optimized for younger, high-digital-literacy users — deep navigation trees, dense UI, low-contrast design patterns •No vertical-specific recommendation engine exists for 50+ travel/wellness preferences •Generic content delivery led to low engagement and poor retention in this segment •Fragmented tools across travel, wellness, and community — no single integrated system •Adoption friction due to usability barriers, not lack of interest
Our Solution
Recommendation engine: AI-driven personalization layer that scores travel, wellness, and activity content against user preference and behavioral signals, surfacing curated rather than generic results •UX layer: Reduced-complexity navigation, accessibility-first interface patterns (font scaling, simplified flows, minimal steps-to-task) •Engagement automation: Klaviyo-driven lifecycle messaging — triggered, segmented campaigns based on user behavior and preference data, rather than blanket broadcasts •Cross-device delivery: Responsive Next.js frontend ensuring consistent experience across desktop, tablet, and mobile •Data layer: Neon DB (serverless Postgres) for scalable, structured user and content data •Infrastructure: AWS-hosted, designed for secure scaling as user base grows
Results
•Increased engagement and repeat usage driven by personalized vs. generic content delivery •Higher digital adoption within the 50+ segment, attributed to friction reduction in UI/UX •Improved retention via targeted, behavior-based engagement campaigns (Klaviyo segmentation) •Stronger community participation through activity/social-discovery features
Conclusion
Greytt addresses a specific gap — AI-personalized lifestyle recommendations built on an accessibility-first technical foundation — rather than retrofitting a general-purpose platform for an underserved demographic. The stack (Next.js + Neon DB + Klaviyo + AWS) supports both the personalization logic and the scale/reliability requirements of a growing user base.
Technologies Used
