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AI training data providers help companies build accurate models through expert data, annotation, and human feedback.
Mercor, Scale AI, Surge AI, and Innodata hold important positions in the AI data services market.
Expert knowledge, multilingual datasets, and model evaluation shape the demand for AI training data.
AI models are only as good as the data they learn from. If you want accurate answers and a model that can handle tricky tasks, you need good data, and a whole industry has grown up around collecting, labeling, reviewing, and preparing it. These days, AI training data providers work with big tech firms, research labs, healthcare companies, and government agencies. And the job has moved well beyond simple labels into expert feedback, language datasets, robotics, and model evaluation.
Scale AI is one of the best-known names in AI data services. It does data annotation, data curation, model evaluation, and reinforcement learning from human feedback (RLHF), and its work feeds into large language models (LLMs), computer vision, robotics, and defense projects. In May 2026, its US Department of War agreement grew from USD 100 Million to a potential USD 500 Million, which puts it in a strong spot for government and defense AI work.
Surge AI’s specialty is human feedback and high-quality data for advanced models. It offers RLHF, expert annotation, preference data, and model evaluation across text, images, audio, and video. Developers who need real human judgment to make their models better tend to rely on it. Surge AI reported USD 1.2 Billion in revenue for FY2024, but keep in mind that this isn’t verified 2026 revenue.
Mercor matches AI companies with experts who can create and review specialist data. Its network draws on people from software development, finance, law, and science, and it handles coding tasks, reasoning tests, expert knowledge datasets, and reinforcement learning projects. Market reports in June 2026 put its annualized revenue run rate above USD 2 Billion. That tells you how quickly it’s moving, though it isn’t audited annual revenue.
Innodata offers data engineering, annotation, enrichment, and AI model evaluation. It serves enterprise AI, generative AI, healthcare, and other specialist areas, and it’s building a bigger presence in robotics and physical AI data. On November 5, 2026, it reported Q3 revenue of USD 95.6 Million, up 53% year over year, and lifted its full-year revenue growth outlook to approximately 42% or more. Clearly, demand for its services is strong.
Appen has been in the game for a long time, working on language data, speech collection, and data annotation. It provides multilingual datasets, search relevance checks, text review, and human evaluation, and its worldwide pool of contributors helps developers build products for people across many languages and regions. If a company needs speech or language data from several markets, Appen is still a sensible choice, and its experience with large projects gives it a firm place in the industry.
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TELUS Digital delivers AI data services through a global workforce. It covers text and speech data, annotation, content review, and human feedback, and it helps AI developers who need language skills and local knowledge in many countries. That reach matters when a project depends on cultural context and accurate language data. It also works with enterprise clients who need big teams and clear quality standards.
iMerit is known for specialist data work in healthcare, computer vision, autonomous vehicles, and robotics. That includes medical image annotation, LiDAR data, sensor data, and expert evaluation for AI models. EXL completed its acquisition of iMerit on August 3, 2026, folding iMerit’s specialist skills into EXL’s wider enterprise data and AI services. For businesses that need domain-specific AI data, the combined company is a stronger option.
Turing focuses on software engineering talent and expert data for building AI models. It provides coding datasets, technical reasoning, model evaluation, and human feedback. AI labs go to it when they want better results in programming, mathematics, and complex problem-solving. With so many technical professionals at hand, it’s a handy partner for advanced language models, and it also takes on AI post-training, where expert review makes a big difference to quality.
micro1 puts AI companies in touch with skilled professionals who can build and assess specialist datasets. It covers coding, expert knowledge, model evaluation, and reinforcement learning, and it’s active in robotics and AI agent development too. Market reports from October 2026 pegged it at a USD 1 Billion annualized revenue run rate and a USD 4 Billion valuation. The growth looks rapid, but these aren’t audited annual revenue figures.
Prolific gives AI projects access to research participants who share human feedback and opinions. It supports model evaluation, preference tests, surveys, and behavioral research, and developers use it to judge model responses and see how people react to different outputs. What makes it different from traditional annotation firms is its focus on research-quality human responses. It’s a good fit for companies that want dependable feedback from real people to check how their AI performs.
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The top AI data providers now meet needs that go far beyond basic labels. Scale AI and Innodata handle large enterprise projects, while Mercor, Surge AI, and micro1 lean on expert knowledge and human feedback. Appen and TELUS Digital are strong on language, and iMerit, Turing, and Prolific serve specialist needs in healthcare, software, and research.
The best providers do more than deliver huge datasets. What matters most is accuracy, expert knowledge, clear usage rights, and steady quality. As AI models take on harder tasks, the companies that supply reliable, relevant data will keep an important place in the global AI market.
1. What are AI training data providers?
AI training data providers collect, label, review, and prepare datasets that help AI models learn, improve, and perform specific tasks.
2. Which are the top AI training data providers in 2026?
The top 10 include Scale AI, Surge AI, Mercor, Innodata, Appen, TELUS Digital, iMerit, Turing, micro1, and Prolific.
3. What services do AI training data companies offer?
These companies offer data annotation, RLHF, expert feedback, multilingual datasets, model evaluation, and specialist data preparation.
4. Why do AI companies need human feedback?
Human feedback helps AI models improve response quality, understand user preferences, reduce errors, and handle complex tasks.
5. How can businesses choose the right AI training data provider?
Businesses should assess data quality, domain expertise, pricing, security, licensing rights, workforce capacity, and project requirements before selecting a provider.