Scale AI

Scale AI is a San Francisco-based artificial intelligence company making an API for validating and training data for applications. It was founded in 2016 by Alexander Wang and Lucy Guo. The company offers users acceleration of AI applications by generating high-quality ground data, with advanced LiDAR, image, video, and NLP annotation APIs to allow machine learning companies to build differentiated models. Scale AI serves industries such as retail, e-commerce, and logistics, with customers that have included Meta, Microsoft, U.S. Army, DoD's Defense Innovation Unit, Open AI, General Motors, Toyota Research Institute, Brex, Instacart, and Flexport.

Scale AI's Data Engine is developed to collect, curate, and annotate data to help train models and evaluate those models. The data engine includes several tools and features to help users, including through data generation, model evaluation, safety, and alignment measurements; data labeling, which combines AI-based techniques with human-in-the-loop to deliver labeled data; and data curation tools to help users identify the highest value data to label. The data engine can be used to help users develop AI systems.

Scale AI's generative AI platform is a full-stack platform developed for enterprise users and powered by the Scale Data Engine. The architecture of the generative AI platform allows users to develop a generative AI system, whether it requires fine-tuning, prompt engineering, security, model safety, model evaluation, and is intended for enterprise apps. This could be to develop machine learning systems for visual collaboration and classification; automated advertising platforms; trend detection; trust and safety, such as detection and removal of unsafe content; claims intelligence; and more.

Scale AI's Spellbook allows users to build, compare, and deploy large language model (LLM) applications. This allows users to upload data and review prompts; compare experiments across LLMs, prompts, and fine-tuning strategies; fine-tune the data to continue to improve the model and its performance; and deploy variants to production with API endpoints with built-in monitoring and analytics.