Delivers advanced search algorithms and machine learning technology that powers product discovery and advertising relevance across Amazon's marketplace, enabling effective customer-product matching and advertising performance.
Last updated Mar 7, 2026 by AI Enrichment
Core technology provider for Amazon's e-commerce search and advertising platform
Amazon A9 is a subsidiary of Amazon that develops and operates the search and advertising technology powering Amazon's e-commerce platform. Founded in Palo Alto, California, A9 is responsible for the product search algorithms that help millions of customers find products on Amazon.com, as well as the underlying technology that drives Amazon's advertising business. The company combines machine learning, natural language processing, and information retrieval to optimize product discovery and ad relevance. A9's technology is integral to Amazon's advertising ecosystem, which has grown into one of the largest digital advertising platforms globally. The search and ranking algorithms developed by A9 directly impact product visibility, making it a critical component for sellers and advertisers on the Amazon marketplace. Beyond product search, A9's innovations extend to visual search, personalization, and recommendation systems that enhance the shopping experience. As a wholly-owned Amazon subsidiary, A9 operates as the research and development arm for Amazon's search and advertising capabilities. While it maintains its own brand identity and engineering culture, A9's work is deeply integrated into Amazon's core commerce and advertising products, including Sponsored Products, Sponsored Brands, and Amazon DSP (Demand-Side Platform).
Core search algorithm that powers product discovery on Amazon.com using machine learning and natural language processing
Ad relevance and ranking systems that determine sponsored product placement in search results
Image-based search technology allowing customers to search for products using photos
Machine learning systems that customize search results and recommendations based on user behavior