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Company Overview

Scale AI is a San Francisco-based artificial intelligence company founded in 2016 that focuses on providing high-quality data annotation, cleansing, and management services to support the training and optimization of machine learning models for enterprises and research institutions.

The company was co-founded by Alexandr Wang, a former MIT student who dropped out of computer science at MIT to focus on entrepreneurship after realizing that artificial intelligence and machine learning would change the world, and Lucy Guo, a former Carnegie Mellon student who is a Thiel Fellow.

Scale AI's core products include Scale Data Engine, Scale Nucleus, and Scale GenAI Platform, which cover image, video, text, and LiDAR data annotation and auditing to help organizations efficiently build large-scale training datasets. The company's customers include tech giants such as OpenAI, Meta, Amazon, and Google, as well as government agencies such as the U.S. Department of Defense, reflecting its leadership in data services.

In June 2025, Meta Platforms acquired a 49% stake in Scale AI for $1.43 billion, reflecting the market's high level of recognition in the AI data infrastructure space. However, Google announced that it would sever its relationship with Scale AI because Meta's acquisition raised concerns about data security and competition.

Despite the challenges, Scale AI plans to continue to expand its products and services to improve the efficiency of training and evaluating AI models and to drive the popularization and adoption of AI technologies.

Core Businesses and Products

1. Scale Data Engine: This is Scale AI's flagship product, providing high-quality data annotation, collection, and management services that support a wide range of data types, including text, images, video, 3D LiDAR, and more. The platform combines AI and human supervision to ensure data quality for a wide range of machine learning tasks from low-volume experiments to large-scale production projects.
2. Scale Nucleus: This is a data visualization and analytics platform that helps developers understand and manage datasets more effectively, address data quality issues, fix model failure patterns, and accelerate the model development process.
3. Scale GenAI Platform (SGP): The platform enables AI teams to build, evaluate, and control intelligent solutions that process enterprise data, perform operations, and continuously improve through human-computer interaction. It supports the development of generative AI applications and helps organizations build intelligent agent systems.
4. Scale Evaluation: This is a platform for testing large language models (LLMs), supporting model evaluation and optimization by identifying model weaknesses through benchmarking and pointing out areas where additional training data is needed.

Core Advantages

• High-quality data annotation: Scale AI combines artificial intelligence with human supervision to ensure high quality data labeling for sensitive areas such as healthcare and autonomous driving.
• multimodal supportIt supports text, image, video, 3D LiDAR and other data types to meet the needs of different AI applications.
• scalability: Scale's platform supports a wide range of machine learning tasks from low-volume experiments to large-scale production projects with excellent scalability.
• Enterprise-level integration capabilities: Scale's platform integrates with an organization's existing machine learning data pipeline, providing flexible workflow options to meet the needs of different organizations.

target user

• Generative AI Company: Such as OpenAI, Meta, Cohere, and Adept, rely on high-quality labeled data to train and optimize their large-scale language models.
• Corporate Customers: Covering industries such as healthcare, finance, automotive, and retail, Scale AI's data platform is utilized for model fine-tuning, document processing, and automated workflow building.
• government organization: For example, the U.S. Department of Defense uses Scale AI's data services for security validation and training of mission-critical systems.
• research organization: For example, the Datta Lab at Harvard Medical School utilizes Scale AI's data platform to accelerate research progress.

development prospect

Scale AI is transforming from a traditional data labeling service provider to a key provider of enterprise-grade generative AI infrastructure.

• Revenue growth: Revenues are expected to reach $2.0 billion in 2025, an increase of more than 1,30% from $870 million in 2024.
• Strategic investments: Meta invests $14.3B in Scale AI in June 2025 for 49% of non-voting shares in the company, further solidifying its position in the AI ecosystem.
• positioning (marketing): Scale AI's full-stack GenAI platform and Scale Data Engine make it a key player in enterprise AI applications, supporting the entire process from model training to inference.