AI-powered travel monetization widget for publishers and creators
Stay22 embeds travel booking widgets into publisher content, generating affiliate revenue without ad-slot friction. The stack (JavaScript/TypeScript + Python + Spark + OpenAI + GCP + Snowflake) reflects a company scaling from simple embeds toward AI-driven personalization and inference at scale. Active adoption of Gemini and NotebookLM, combined with pain points around low-latency ML inference and production-ready pipelines, signals a shift toward real-time, LLM-powered recommendations—a step beyond static monetization.
Notable leadership hires: Director of Engineering, Head of Innovation Operations
Stay22 is a content monetization platform based in Montreal that helps publishers, creators, and event platforms generate revenue from travel-related content. The product surfaces high-intent travel booking opportunities (accommodations, transportation) as embedded widgets, capturing affiliate commissions without disrupting editorial experience or consuming ad inventory. The company operates at significant scale: 4 billion+ user reach and over $1 billion in travel bookings facilitated. Engineering and ops hiring remain active, with leadership roles including Director of Engineering and Head of Innovation Operations, indicating product and infrastructure maturation.
JavaScript, TypeScript, Node.js, and Python for application logic; Apache Spark and MongoDB for data processing; Snowflake for analytics warehouse; OpenAI for AI features; GCP as primary cloud provider; HubSpot and Zendesk for customer operations.
Adopting Gemini and NotebookLM. Active projects include an LLM internal client, unified data and AI agent ecosystem, and low-latency inference optimization—indicating shift toward personalized, AI-driven booking recommendations.
Other companies in the same industry, closest in size
Stay22's technology stack, projects, and hiring signals are inferred from public hiring and company data — career pages, public listings, and company web presence — then clustered and de-duplicated. Figures are estimates that refresh over time. Read our full methodology →
This is not an official vendor or customer list. It is a technology-adoption signal inferred from public data, intended for B2B research.