As of May 2026, there are 62 SaaS companies in Data Labeling Software. They have combined revenues of $4.5B and employ 15.1K people. They have raised $1.9B and serve 502.5K customers combined.
Data labeling software is designed to facilitate the process of annotating data, which is crucial for the development of machine learning and artificial intelligence models. Users of this software can label various data types, including images, audio, and text, providing the necessary annotations that allow algorithms to recognize patterns and make predictions. The software streamlines workflows, enabling large datasets to be processed efficiently and ensuring data quality through collaborative tools and automated features. Typical use cases for data labeling software include applications in computer vision for object detection, natural language processing for text classification, and audio analysis for speech recognition. With common features like user-friendly interfaces, quality control mechanisms, and integration capabilities with machine learning frameworks, this software empowers data scientists, AI developers, and researchers to prepare their data sets comprehensively. The primary buyers often include tech companies, research institutions, and enterprises looking to enhance their AI solutions and analytics capabilities.
Sorting: Highest -> Lowest
Showing 10 of 9 companies ranked by annual revenue.
London, United Kingdom
Prolific helps AI developers, researchers, and organizations easily access the highest-quality human data. It is a technology company building the biggest pool of quality human data in the world and the ultimate platform to access it.
San Francisco, California, United States
Developer of a data training platform intended for computer vision machine learning applications. The company's platform offers a visual workflow interface and system of record for the data labeling process, using annotation tools as well as quality control functionality and performance analytics, enabling business to reduces model development times and empowers data science teams to build great machine learning applications.
Lisbon, Portugal
Lyzer is an AI-powered data analytics and decision intelligence platform that helps businesses analyze data, generate insights, and automate data-driven decisions without requiring deep technical expertise.
New York, New York, United States
DataForce delivers high-quality, multimodal training data and services to power the next generation of AI. From large language models to voice, image, and video generation, DataForce supports AI innovators in tech, life sciences, automotive, and beyond with scalable, secure solutions for development, testing, and safety. Backed by cutting-edge technology and over one million data contributors, DataForce helps ensure AI systems are accurate, adaptable, and ready for real-world deployment. DataForce is part of TransPerfect, the world’s largest provider of language and AI solutions for global business, with offices in more than 140 cities worldwide. Learn more at www.dataforce.ai. Contact: [email protected]
San Francisco, California, United States
SuperAnnotate is the leading platform for building, fine-tuning, iterating, and managing your AI models faster with the highest-quality training data. With advanced annotation and QA tools, data curation, automation features, native integrations, and data governance, we enable enterprises to build datasets and successful ML pipelines. Partner with SuperAnnotate’s expert and professionally managed annotation workforce that can help you quickly deliver high-quality data for building top-performing models.
San Jose, California, United States
Developer of vision processing technology intended for autonomous vehicles. The company's auto labeling system produces training data with minimal human input, and a semi-supervised learning-based SVNet training tool allows customers to enhance SVNet by themselves during mass production projects, enabling autonomous vehicles to reach the next level of safety, accuracy and driver convenience through proper real-world detection, tracking, segmentation and classification.
London, London, United Kingdom
DataEQ uses a Crowd of humans to label millions of unstructured data points. This trains our cutting-edge AI to do remarkable things. This unique combination means we produce the highest quality customer data available. You can trust our data to measure and improve your CX, accurately monitor market conduct and deliver world-class customer service.
Seattle, Washington, United States
Developer of a Crowd-as-a-Service intelligent data platform intended to accelerate enterprise data training and modeling. The company's platform combines machine learning and data science with crowd-sourcing to help companies to easily manage their global data collection and data enrichment efforts, enabling enterprises to improve quality, scalability and time-to-market for their artificial intelligence and natural language processing applications.
- Must provide tools for labeling diverse data types including images, text, and audio. - Should support both manual labeling and automated annotation processes. - Must include collaboration features for teams to work on data labeling tasks. - Must ensure quality control mechanisms to verify the accuracy of labeled data. - Not just a data management tool; must also provide data annotation capabilities.
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