Case Study - AI-Powered Event Catalog

The Challenge

KEAI addressed a critical problem in urban cultural discovery: event information was scattered across countless social media platforms and websites, making it nearly impossible for people to find experiences aligned with their interests. Traditional event platforms failed due to the "empty platform syndrome"—without content, users wouldn't engage, and without users, event organizers wouldn't contribute content, creating a vicious cycle of abandonment.

AI-Driven Solution Architecture

KEAI broke this cycle by implementing a sophisticated automated content acquisition and intelligent categorization system. The platform deployed web scraping technology to continuously monitor ticketing platforms, cultural venues, and event websites across Santiago, automatically populating the database with comprehensive event data. This eliminated the dependency on manual content creation while ensuring the platform remained consistently updated with fresh, diverse content.

Advanced AI Categorization and Personalization

The platform implemented dual AI systems for intelligent content organization. A language model-based categorization system analyzes event descriptions using natural language processing to automatically assign appropriate categories and tags. Additionally, KEAI employs vector embedding technology that converts event descriptions into mathematical representations, enabling semantic similarity matching that identifies conceptual relationships between events beyond simple keyword matching. This approach allows the system to recognize that a "silent disco in a museum" shares DNA with both electronic music and cultural art categories.

Intelligent Recommendation Engine

KEAI's recommendation system combines collaborative filtering with semantic analysis to deliver personalized event suggestions. The platform tracks user favorite patterns to identify behavioral clusters, while simultaneously using vector similarity to match users with events based on deeper conceptual relationships rather than just categorical overlap. This dual approach enables recommendations that feel both data-driven and culturally intuitive, suggesting experiences users might not have discovered through traditional category browsing.

Results and Impact

By leveraging AI automation, KEAI successfully launched with a fully-populated event database from day one, immediately providing value to users and attracting event organizers to claim and manage their listings. The automated categorization system processes thousands of events with consistent accuracy, while the intelligent recommendation engine creates increasingly personalized user experiences that grow more sophisticated as the platform learns from user interactions and Santiago's evolving cultural landscape.