Locate.dance — AI Discovery & Social Graph

Locate.dance is an AI-powered discovery platform that turns fragmented dance listings into an aggregated social graph of events, organizers, venues, locations, and dance styles. Automated discovery, AI parsing, and n8n workflows support a searchable experience for finding socials, classes, and festivals across local dance communities.

Turning a fragmented scene into connected data

Dance communities publish information across event pages, promotional images, and independent organizer websites. Locate.dance brings that information into a shared structure, connecting each event to its organizer, city, venue details, genres, and scheduled occurrences. This connected model provides the foundation for exploring a dance scene by place, style, and time.

Automated discovery and AI parsing

The ingestion pipeline collects events from configured sources and uses AI to interpret event descriptions and promotional images. Structured extraction identifies pricing, currencies, and dance genres, while producing consistent descriptions that retain the source details. Organization matching, venue geocoding, and source-based duplicate checks prepare the results for discovery.

Workflow automation with n8n

n8n supports the automation layer around event discovery and AI parsing. Together with the ingestion pipeline, these workflows connect source collection, enrichment, and processing into a repeatable publishing process, reducing the need to rebuild listings by hand.

Recurring events, organized for discovery

The platform models recurring events and their individual occurrences, with support for weekly and monthly patterns. Visitors can browse by country, city, and dance style, then open an event for dates, pricing, venue information, and organizer context. The result is a discovery experience grounded in when and where people can actually dance.

From static schedules to structured pages

The Schedule to Page tool extends AI extraction to uploaded schedules. Images, PDFs, and text can be interpreted as structured sessions with dates, times, locations, instructors, and skill levels. The workflow is designed to turn dense festival programs and class timetables into readable, shareable web pages.

Engineering the discovery layer

The product combines a Next.js interface, a relational PostgreSQL data model, AI extraction, and workflow automation. Its value lies in bringing collection, interpretation, event relationships, and visitor-facing discovery into one system: a reusable information layer for the social dance community.