Scale AI helps enterprises to unlock the potential of their data by developing, testing, and deploying custom Generative AI applications. Its GenAI Platform integrates APIs, SDKs, and secure cloud deployment, optimizing retrieval-augmented generation (RAG) pipelines and utilizing both open-source and proprietary models for scalable, enterprise-ready AI innovation.
Scale AI offers AI infrastructure to help enterprises convert raw data into production-ready, high-quality datasets. They specialize in data labeling and data annotation, addressing a major obstacle when training accurate, reliable machine learning models across all types of industries.
In addition to data labeling, Scale AI is capable of innovation with the same infrastructure, providing solutions for generative AI, Autonomous Systems, and government use cases. Their main tools, Scale Data Engine, Scale Donovan, and Scale GenAI, help organizations build, evaluate, and deploy scalable AI solutions that provide speed, security, and enterprise-level performance across any cloud environment.
| Legal Name | Scale AI, Inc |
| Headquarters | San Francisco, California, United States |
| Business Model | B2B |
| Founding Date | 2016 |
| No. of Employees | Approx. 900 (As of 2025) |
| Alexandr Wang | Founder |
| Jason Droege | Chief Strategy Officer |
| Dennis Cinelli | Chief Financial Officer |
| Vijay Karunamurthy | Field Chief Technology Officer |
| Jon Wilfong | Senior Vice President, Field Operations |
| Tom Channick | Vice President, Communications |
| Brooke Peterson | Vice President, GTM – International |
| David Kinsella | Vice President, BizOps & Strategy |
| Daniel Berrios | Director of Product |
| Lauren Escher Lennon | General Counsel & Corporate Secretary |
Scale AI earns revenue from two sources. Scale AI's primary model of revenue generation is subscription access to its AI infrastructure platform, with pricing based on the total amount, complexity, and customized needs associated with data-processing requirements.
The second potential revenue stream comes from large enterprises looking for AI support contracts, managed AI, digital labor, annotated data, and custom model satisfaction (i.e., designing the initial model and evaluating its effectiveness).
Client Segments: Primarily large corporations, SMBs, government and public agencies, some education institutions, and vertical-specific clients in energy, finance, healthcare, retail, manufacturing, and others.
Target Companies: Large Corporations, SMBs, Public Sector Organizations, Tech Startups, E-commerce Platforms, and Others.
Target Geography: Global, including North America, Europe, Asia-Pacific, Latin America, the Middle East, and Africa.