Vody

Vody is a generative AI company that provides an AI-optimized data layer for the retail and e-commerce industry. Founded in 2016 by Stephanie Horbaczewski and Jeremy Houghton, the Los Angeles-based company aims to transform how retailers structure and utilize their product data. Horbaczewski, a serial entrepreneur, previously founded and sold StyleHaul, an experience that informed Vody's focus on solving data challenges in commerce. Co-founder Andrew Cronk serves as CTO, bringing over 30 years of experience in software engineering and AI, including leading machine learning and search at Etsy.

The company's core service is an adaptive AI infrastructure designed specifically for commerce. It addresses the common issue where messy or inconsistent catalog data leads to poor search results and customer experiences. Vody's platform ingests unstructured and structured product data from retailers and uses proprietary, fine-tuned large language and vision-language models to enrich, structure, and personalize it at scale. This process involves Agentic AI Data Extraction to create AI-ready product records and GenAI Enriched Attributes to add compelling descriptions and fill in missing details. A key feature is its 'Search Conversion Memory,' which combines Graph Machine Learning and Generative AI to learn from customer search behavior, aligning product keywords with how shoppers actually look for items.

Vody's business model is centered on providing this data layer as a service to enterprise retailers, enhancing their existing systems without requiring a complete overhaul. The platform is designed for seamless integration and can be deployed in as little as seven weeks. By making product catalogs more searchable and optimized for conversion, Vody helps retailers improve search performance, personalization, and ultimately, revenue. The company has raised a total of $10 million in a Series A funding round in October 2020.

Keywords: generative AI, retail technology, data enrichment, e-commerce search, product data management, catalog optimization, personalization engine, machine learning, contextual search, AI infrastructure