Stay22 embeds travel booking widgets into publisher content, capturing high-intent readers without ad placements. The stack reveals a QA-heavy testing discipline (Playwright, Cypress, Selenium, TestCafe) paired with a substantial data infrastructure (Snowflake, MongoDB, Spark, Grafana, Looker), and active investment in LLM-driven features (OpenAI, LLM gateway, AI agent generation, redirection engine). The project roadmap—spanning data pipeline reliability, model inference optimization, and widget integration—shows the company scaling from a placement layer toward an AI-powered affiliate logic engine, with hiring skewing senior (13 of 24 roles) and concentrated in engineering and data.
Notable leadership hires: Head of Data
Stay22 operates a content monetization platform connecting publishers and creators to travel partners. The product layer sits between publisher content and booking networks, capturing reader intent via AI-powered redirection and affiliate links without consuming page real estate. The company has processed over $1 billion in travel bookings and reached more than 4 billion users. Technical operations span North America, with Montreal as headquarters and Canadian hiring focus. The org is engineering-forward, with active development on LLM inference, data pipeline architecture, public API exposure, and dashboard automation across HubSpot, Mixpanel, and Looker.
Stay22 uses Node.js, Python, TypeScript, and JavaScript for application logic; Snowflake and MongoDB for storage; Spark for data processing; GCP for cloud infrastructure; Playwright, Cypress, and Selenium for testing; and OpenAI for AI features. Analytics and BI run on Grafana, Looker, and Mixpanel.
Key projects include an AI redirection engine, LLM gateway infrastructure, widget integration platform, data scraping for partner APIs, public API launch, and dashboard automation. Data pipeline reliability and model inference latency are active focus areas.
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