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 19 companies ranked by annual revenue.
Joinville-le-Pont, Île-de-France, France
Founded seven years ago, Infoscribe. ai is the AI branch of Infoscribe SAS, a dynamic French company specializing in the provision of high-quality data labeling services. 𝐎𝐮𝐫 𝐄𝐱𝐩𝐞𝐫𝐭𝐢𝐬𝐞: Thanks to our innovative services and dynamic teams, we help companies accelerate the development of their Computer...
Herzliya, Israel
DataLoop's data management and annotation platform streamlines the process of generating machine consumable datasets out of raw visual data.
Heidelberg, Baden-Württemberg, Germany
Better datasets create better machine learning models, enabling better products.Quality Match verifies and improves the quality of datasets for computer vision and machine learning. This can be applied to: - dataset architecture (are my dataset and taxonomy good?) - measure the quality of annotation providers (is the quality as advertised?) - evaluate model performance (when does the model fail?) - supervise model-drift (is the model still working?) and many more use cases. We focus on creating actionable, quantitative metrics on dataset quality, such as: - representativeness (bias analysis) - accuracy (labels, detection rates and geometric properties of annotations) - and ambiguity (edge cases due to bad data or bad taxonomies). We do this by breaking down quality questions into a large decision tree where each node is either a training-free, unambiguous crowd-sourcing task which is repeated until statistical significance, or a pretrained machine learning model with known confusion matrix. We call this system our Annotation Quality Engine. To verify your data, we help you verifying and integrating your metrics into our Annotation Quality Engine which you will then be able to query through a simple REST API.
MIDDLETOWN, Delaware, United States
Dify is an open-source development platform designed to streamline the creation and management of AI applications utilizing large language models (LLMs). It integrates Backend-as-a-Service (BaaS) and LLMOps capabilities, offering tools for visual prompt orchestration, long-context integration, data annotation, and API-based development. Dify supports a variety of LLMs, including GPT, Mistral, and Llama, and facilitates rapid prototyping to production, making it accessible for both technical and non-technical users to define, deploy, and improve AI applications efficiently.
Berlin, Berlin, Germany
Hasty is a Vision AI company helping humans teach machines to see the world. Based in Berlin, Hasty supports Vision AI practitioners by developing best-in-class annotation tools that are supported by a community of machine learning engineers, data scientists and software developers. Hasty's next-generation annotation tool labels data in one-tenth the time through self-learning AI-assistants and agile machine learning that provides rapid feedback so engineers can validate and adapt models as they work. For more information, visit https://hasty.ai/ or follow us on Twitter @hasty_ai.
San Francisco, California, United States
Frontier coding data for training and evaluating LLMs
Bengaluru, Karnataka, India
* End-to-end DL/ML-based AI solutions & SaaS or On-Premises deployments for image procession and machine vision, language processing, recommendation systems and forecasting * Recommending the right solution and the best approach custom designed for the analytics problem at hand * Data and Image Annotation services * Our product VAST - CCTV Intelligence is a “Video Surveillance as a Service” (VSaaS) product that provides industry's most advance, accurate and useful analytics for manufacturing industries and for commercial offices and monitoring. Our existing customer list in India includes marquee names in the industry.
Atlanta, Georgia, United States
swivl is simplifying AI training. In general, data scientist typically spend 80% of their time on non-value-added task such as finding, cleaning and annotating data. Our SaaS platform helps teams outsource these data annotation tasks to a diverse crowd to close the feedback loop in a cost-effective way. This involves the action of training, testing, and deploying machine learning models with an emphasis on natural language processing, audio, and generalized data categorization.
Paris, Île-de-France, France
Lettria brings business and technology teams together on a no-code collaborative framework, to guide them through every step of a text processing project : Step 1 / Data Collection & Prepration Step 2 / Data Annotation & Ontology Management Step 3 / NLP Engine Customization Step 4 / Demo and output management Get started for free on our website : www.lettria.com Short on time? Ask our support team to help you kickstart your project. 👀 [email protected] 👀
- 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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