Top 50 Data Labeling Service

This startup provides an AI-powered data labeling platform specifically for healthcare applications. It enables developers and R&D teams to efficiently create high-quality training data for supervised machine learning models in medicine, accelerating AI development for improved patient outcomes and operational efficiency.

Sapien provides custom data collection and labeling services for AI training, utilizing a decentralized workforce and a gamified platform to ensure high accuracy and scalability. The company addresses the challenge of obtaining quality training data for large language models by offering real-time human feedback and tailored annotation solutions across diverse industries.

Datasaur provides a customized platform for data labeling, utilizing automation to enhance the efficiency of natural language processing (NLP) projects by up to 9.6 times. The company develops tailored large language models (LLMs) that address specific organizational data challenges, significantly reducing project costs by up to 70%.

Isahit provides an ethical data labeling platform that utilizes a human-in-the-loop approach to ensure high-quality, bias-free annotations for AI training across various datasets, including computer vision and natural language processing. The platform addresses the need for accurate data labeling while creating meaningful job opportunities in developing countries, thereby promoting social impact.

Pareto.AI is a talent-first platform that connects AI companies with the top 0.01% of expert-vetted data labelers to provide high-quality training data for AI and LLM models. By offering same-day access to specialized teams and precise data labeling, the platform addresses the need for reliable and efficient data collection in AI development.

The company provides an on‑demand data annotation platform that lets machine‑learning engineers upload audio, text, or image assets via a web UI or API and receive labeled data in standard formats ready for training pipelines. A global pool of vetted contributors performs task‑specific labeling, augmented by AI‑driven pre‑labeling and multi‑pass quality assurance, while role‑based access controls and encryption ensure compliance.

BeyondML provides a cloud‑based crowdsourcing platform that lets AI teams upload raw data and define annotation tasks via a web UI or API. A global pool of vetted annotators completes image, video, text, and audio labeling with built‑in quality‑control workflows, delivering export‑ready datasets for direct integration into model‑training pipelines.

Surge AI provides a data labeling platform that utilizes human feedback to enhance the training of large language models (LLMs). By delivering high-quality labeled data, Surge AI enables organizations to improve the accuracy and performance of their NLP applications.

HumanSignal provides a data labeling platform that combines automation and human oversight to prepare training data, fine-tune large language models, and evaluate AI outputs. This solution enhances model accuracy and efficiency while ensuring compliance and data security across various use cases and data types.

FastLabel provides a high-quality annotation platform that specializes in creating and managing labeled datasets for AI applications, ensuring a data quality delivery rate of 99.7%. The service addresses the challenge of obtaining reliable training data by offering tailored annotation solutions, MLOps support, and access to over one million rights-cleared datasets.

Werkit provides scalable, managed teams for data processing and human-in-the-loop tasks, specializing in computer vision and natural language processing. By delivering high-quality data labeling and processing solutions, Werkit enhances data accuracy for clients in industries such as healthcare, fintech, and media, enabling them to focus on core innovations.

Liberty Source PBC provides human-in-the-loop data services that deliver high-accuracy labeling, annotation, and testing for AI and machine learning applications, particularly in autonomous systems and language model fine-tuning. By employing a US-based workforce, the company ensures data security and compliance while enhancing model performance through precise data preparation and quality assurance.

PublicAI provides a decentralized Web3 platform that connects AI developers with a verified global pool of contributors for multimodal data collection and annotation. The system uses AI‑assisted pre‑labeling, on‑chain validator review, and cryptocurrency payments to deliver high‑accuracy text, audio, video, image, and 3D LIDAR datasets via a scalable API suite.

Rapidata offers a platform for large‑scale human annotation and real‑time feedback, enabling AI developers to collect labeled data and evaluate model performance quickly. Its network of annotators across 192 countries provides unbiased, high‑quality labels for tasks such as classification, segmentation, ranking, and RLHF/DPO. The service integrates via API or web UI, delivering fast, cost‑effective insights to accelerate model training and deployment.

Perle AI provides expert-in-the-loop data annotation and training services to accelerate AI model learning for enterprises. The company leverages a vetted network of domain experts to deliver precise, multi-modal data labeling and human feedback for model alignment and safety. Their modular platform offers flexible workflows and quality assurance to ensure high-quality training data for rapid AI iteration.

Labelbox operates a data training platform that utilizes AI-assisted labeling and a global network of experts to provide high-quality data curation and evaluation for machine learning applications. This platform addresses the challenge of efficiently managing large-scale data labeling and evaluation, enabling businesses to accelerate model development and improve AI performance.

This company provides end-to-end data services focused on improving AI model performance through high-quality inputs. Their process involves curating global datasets, enriching content with expert, context-aware labeling, and implementing clean, normalized data for model ingestion. They aim to solve the data quality issues that cause many AI projects to fail during deployment.

Datum AI provides data annotation and collection services to accelerate machine learning development. They specialize in sourcing and labeling diverse data types and offer expertise in Reinforcement Learning from Human Feedback (RLHF) and Supervised Fine-Tuning (SFT) for generative AI.

Karya provides data generation and annotation services to build culturally sensitive and powerful AI models. They leverage a people-centric platform to deploy tasks and collect diverse, high-quality datasets across numerous languages and dialects. The company focuses on ethical data practices while enabling economic opportunities for rural workers through digital task deployment.

Refuel provides an end-to-end platform for cleaning, structuring, and transforming enterprise data using customized Large Language Models. Users instruct the AI via natural language and feedback to automate data labeling, enrichment, and quality assurance tasks. The platform manages LLM customization and deployment for both streaming and batch workloads while ensuring data security and control.

This company provides global AI data solutions, specializing in data collection, annotation, and processing for machine learning applications. They offer services including image, video, and text annotation, as well as content moderation and product categorization. The focus is on delivering high-accuracy, scalable data sets with rapid turnaround times for computer vision and NLP projects.

Alienbyte provides a web application for the rapid labeling of 3D tomographic images, facilitating collaboration between radiologists and AI engineers. The platform supports no-code model deployment and is compatible with major data storage solutions like AWS and Azure, streamlining the workflow in medical imaging development.

Clarifai offers an end-to-end AI lifecycle platform that automates data labeling, model training, and deployment, enabling organizations to build and operationalize AI applications efficiently. By standardizing workflows and optimizing compute resources, the platform reduces development time and costs, allowing enterprises to scale AI solutions rapidly.

Lemissa provides an open and equitable outsourcing platform for complex data processing tasks that require human intervention. The service connects businesses with a flexible, managed community of independent workers for data labeling, cleaning, and entry necessary for AI model training and database creation. This model allows clients to dynamically scale their data workforce to meet fluctuating business needs without compromising performance or operational equity.

V7 is an AI training data platform that provides high-quality image and video annotations for computer vision models, utilizing AI-assisted labeling tools to enhance accuracy and efficiency. The platform addresses the challenge of slow and error-prone data labeling processes by streamlining workflows and enabling rapid deployment of training data.

Perle AI provides an expert-in-the-loop data annotation and training platform that links vetted domain specialists with enterprise AI pipelines for multi-modal models. The modular workflow supports data acquisition, labeling, versioning, bias auditing, drift detection, and RLHF, delivering real-time visibility, audit trails, and continuous model refinement. By handling data management complexities, it enables AI teams in technology, healthcare, legal, finance, and research to scale high-quality, compliant training data.

The startup develops an AI platform that optimizes computer vision by transforming large foundation models into smaller, task-specific models. This approach reduces resource consumption and accelerates the deployment of computer vision applications for clients.

Provides a platform for managing, classifying, and analyzing photo and video data using automated labeling, insight extraction, and synthetic data generation. It streamlines workflows in research and development by reducing the time spent on manual data processing and enabling more efficient data-driven decision-making. Upcoming features will include bias detection and community collaboration tools to further enhance data analysis and sharing.

understand.ai provides AI-driven data annotation solutions specifically designed for autonomous driving applications. Their automated annotation platform enhances the efficiency and accuracy of large-scale validation projects, addressing the high costs and time constraints associated with manual labeling.

Ultralytics Platform provides a cloud‑native workspace that integrates computer‑vision data labeling, GPU‑accelerated model training, and global deployment into a single environment. It supports browser‑based annotation with SAM and YOLO auto‑labeling, export to formats such as ONNX, TensorRT, and CoreML, and auto‑scaling endpoints with real‑time monitoring and team collaboration tools.

Snorkel Flow is an AI data development platform that enables data scientists to programmatically label and annotate large datasets, significantly reducing the time required for data preparation. By leveraging domain knowledge and automated techniques, the platform enhances the accuracy and efficiency of training data for specialized AI applications in fields like bioinformatics and natural language processing.

Tasq.ai provides a configurable AI flow platform that integrates decentralized human guidance with best-in-class machine learning models to enhance data labeling and model accuracy. The platform addresses the challenges of scaling AI processes and ensuring ethical oversight, enabling organizations to optimize their AI workflows efficiently.

The startup offers an AI platform that provides human-annotated data for training machine learning models through a decentralized marketplace of skilled annotators. This approach ensures high-quality, scalable, and cost-effective labeled datasets, addressing the challenge of acquiring accurate training data for AI applications.

SuperAnnotate offers an integrated AI data platform for efficient multimodal data annotation and management. It streamlines the entire data lifecycle, from custom annotation workflows to quality assurance, accelerating AI model development for use cases like LLMs and RAG.

Kriptos utilizes AI algorithms to automatically analyze, classify, and label sensitive data, ensuring compliance with data protection policies. This technology enables organizations to manage access and usage of their critical information, reducing the risk of data breaches and enhancing overall cybersecurity posture.

Cartex Data provides curated training datasets and labeling services for generative AI models such as GPT. By combining human‑expert data preparation, reward model tuning, and domain‑specific pipelines, it enables enterprises to improve AI accuracy and speed for automation, decision‑making, and robotics. Access is offered via subscription and custom project fees.

The startup offers a data annotation platform that utilizes machine learning optimization techniques to enhance the accuracy and efficiency of labeled datasets. This platform addresses the challenge of time-consuming and error-prone data preparation processes, enabling organizations to accelerate their AI model training.

The startup offers a CRM system designed for data annotation and storage specifically for AI training datasets. This solution streamlines the management of labeled data, enhancing the efficiency and accuracy of machine learning model development.

The startup has developed an online crowd-working platform that connects enterprise clients with skilled individuals on the autism spectrum for tasks such as web research and data management. This platform enables companies to efficiently fulfill their data-labeling needs while providing meaningful employment opportunities for autistic workers.

AI-driven platform that generates high-quality, labeled datasets tailored for machine learning applications. It streamlines the data preparation process, reducing the time and resources required to create "golden datasets" that improve model accuracy and performance.

Unitlab offers a collaborative, AI-powered data annotation platform that utilizes auto-annotation tools to enhance labeling efficiency by 15 times while reducing costs by 80%. The platform addresses the challenge of slow and expensive data preparation for machine learning by enabling seamless collaboration between AI and human annotators for high-quality dataset creation.

The startup operates a cloud-based computing platform that provides AI-driven solutions for researchers and enterprises, focusing on large language model development, programmatic data labeling, and machine learning testing. It offers high-performance computing resources, including access to powerful GPUs and virtual machines, while promoting e-waste reduction through environmentally friendly practices.

Soul AI connects AI companies with a global network of domain experts for specialized data annotation and model training. This platform provides access to accurately annotated datasets across diverse industries, accelerating AI development cycles.

SuperAnnotate is an AI data platform that integrates dataset creation, curation, and model evaluation into a single workflow, enabling users to build and fine-tune high-quality models efficiently. The platform addresses the challenges of data annotation and model performance assessment by providing customizable tools and access to a global marketplace of trained annotation teams.

Zirqat provides AI‑powered digital product labels that ensure compliance with regulations such as the EU Digital Product Passport. The platform delivers real‑time monitoring, analytics on consumer behavior, and transparent labeling to strengthen customer trust and drive revenue growth. Its scalable big‑data solution enables businesses to make data‑guided decisions and expand sustainably.

Precepi offers an AI-powered platform that extracts and structures regulatory labeling data, health authority reviews, and past negotiation documents from multiple markets. It enables labeling teams to query the database in natural language and receive summarized patterns, source citations, and comparative language views, accelerating regulatory decision‑making. The service is provided on a subscription basis.

Superb AI offers an end-to-end training data platform that automates data preparation and curation, enabling rapid and systematic dataset creation for AI model development. This solution addresses the inefficiencies in data handling, allowing organizations to streamline their AI workflows and enhance model deployment speed.

Rabbitt.AI develops reliable generative AI solutions by leveraging enterprise data to create custom large language models and high-quality training datasets. The platform addresses the challenge of inconsistent AI performance by providing precise data annotation and AI-assisted quality checks, ensuring accurate and effective model outputs.

Cappersoft provides high-quality annotated datasets for training AI and machine learning models, specializing in image, video, text, audio, and document processing. The company addresses the need for precise data labeling to enhance the accuracy and efficiency of AI applications across various industries, including automotive, healthcare, and e-commerce.

The startup specializes in artificial intelligence and data labeling, providing live image annotation, audio transcription, and local language services for machine learning applications. By offering quality data labeling, the company enables motivated young individuals to gain work experience while addressing the demand for accurate training datasets in AI development.