Real-time ticket intelligence,seat by seat.
Ticketmaster's seat-level data only appears when a user hovers and clicks on the interactive map, so we built a scraping system that does exactly that. Puppeteer drives the SVG seat maps, AI parses the event listings, and a multi-layer anti-detection stack keeps it running in production.
The data livesbehind a hover.
Ticket resale markets run on real-time pricing intelligence, but Ticketmaster does not expose seat-level data through any API. The only way to see accurate ticket inventory is the interactive seat map: an SVG interface that reveals seat details only when a user hovers and clicks.
Traditional scraping fails here. The data is not in the HTML; it loads dynamically as each seat element is touched. And Ticketmaster's bot detection, from fingerprinting and CAPTCHAs to rate limiting and IP blocking, stops conventional scrapers before they get close.
Seat data appears only on hover and click interactions. Traditional HTML parsing captures nothing.
Ticketmaster does not expose seat-level inventory. Only aggregate data is available through official channels.
Fingerprinting, CAPTCHAs, rate limiting, and IP blocking make conventional scraping extremely difficult.
Event listings have to be found and parsed automatically before any seat map can be reached.
A scraper that moveslike a real visitor.
The system simulates human interaction on Ticketmaster's seat maps. Puppeteer with stealth plugins drives the hover and click sequences, ChatGPT parses event data out of varied HTML structures, and residential proxies keep every request looking like a home connection.
AI models parse Ticketmaster's HTML event listings to extract titles, dates, venues, and URLs, with extraction that handles varied page structures.
Puppeteer navigates to each seat map and simulates hover and click interactions on the SVG elements, extracting seat-level data that API scraping cannot reach.
The stealth plugin patches automation tells such as navigator.webdriver, user agents and viewport sizes rotate per session, and the browser timezone is matched to the proxy IP's geolocation.
Bright Data residential IPs rotate per request so traffic reads as real home connections, an ad-block plugin removes tracking scripts, and a reCAPTCHA plugin handles challenges when they appear.
Classifies tickets into four categories, Available, Resale, Accessible (ADA), and VIP, each with its own visual indicators on the map and its own data pipeline for analysis.
Extracted data is upserted immediately to Supabase PostgreSQL across 6 tables, with deduplication and conflict resolution on seat identifiers keeping the data clean.
Seat-level data,synced as it appears.
Because the scraper reads the interactive seat maps rather than any API, it captures inventory that conventional scraping cannot reach, and the anti-detection layers keep it running in production rather than getting blocked.
Four weeksto production.
From analysing the seat map rendering to a monitored production scraper, one phase per week.
Discovery and core development
Analysed Ticketmaster's seat map rendering, identified the SVG interaction patterns, and built the Puppeteer scraper with stealth plugins.
AI and anti-detection
Integrated ChatGPT for parsing event data out of the HTML, then added Bright Data residential proxies, user agent rotation, and browser fingerprint randomisation.
Database and integration
Set up the Supabase schema with 6 tables, implemented the upsert logic with deduplication, and configured node-cron for scheduled runs.
Production and monitoring
Deployed to production with Winston logging, error handling with 5-retry logic, and real-time monitoring dashboards.