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Top 10 Companies Offering Large-scale AI Data Annotation and Labelling Services in the World 2024

Short answer. The leading large-scale AI data annotation and labelling companies in the world in 2024 were Scale AI, Appen, TELUS International (now TELUS Digital), Turing, TaskUs…

Kelvin T. · August 2026 · 11 min read

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Short answer. The leading large-scale AI data annotation and labelling companies in the world in 2024 were Scale AI, Appen, TELUS International (now TELUS Digital), Turing, TaskUs, Centific, DataForce by TransPerfect, Invisible Technologies, iMerit, and Sama. Scale AI ranks first in this editorial list because its 2024 funding, market valuation, major AI-lab relationships, and ability to supply large volumes of high-quality human-labelled data placed it at the centre of the global AI-training-data market. [1][2]

What counted as a “top” AI data annotation company in 2024?

In 2024, data annotation was no longer limited to drawing boxes around objects or transcribing speech. The rise of generative AI created demand for multimodal labelling, reinforcement learning from human feedback (RLHF), prompt-response evaluation, domain-expert data creation, red teaming, safety evaluation, and large-scale quality assurance. At the same time, computer vision, autonomous driving, mapping, speech, healthcare, and enterprise AI still depended on traditional high-volume annotation.

This ranking therefore evaluates companies on a broader 2024 definition of large-scale AI data services: the ability to source and manage large workforces, handle multiple data modalities, deliver at enterprise scale, support advanced AI use cases, maintain quality and security processes, and demonstrate meaningful market impact. It is an editorial ranking rather than an official audited market-share table. Where later disclosures describe 2024 performance, they are used only to illuminate what the company achieved during 2024.

Ranking methodology

Scale and delivery capacity: size and reach of managed workforces, crowds, expert networks, and delivery operations.

Market impact in 2024: enterprise adoption, disclosed revenue or funding signals, major customer relationships, and analyst recognition.

Breadth of annotation capabilities: text, image, video, audio, LiDAR, sensor, geospatial, multilingual, and multimodal data.

Generative-AI readiness: RLHF, supervised fine-tuning data, expert data creation, model evaluation, safety, red teaming, and hallucination mitigation.

Technology, quality, and governance: proprietary platforms, AI-assisted annotation, workforce controls, security, and repeatable quality processes.

2024 ranking at a glance

  • Rank
  • Company
  • Base
  • Why it stood out in 2024
  • 1
  • Scale AI
  • United States
  • Frontier AI training data, enterprise annotation, expert human feedback
  • 2
  • Appen
  • Australia / United States
  • Global crowd, multilingual annotation, mature DAL platform
  • 3
  • TELUS International (TELUS Digital)
  • Canada
  • Massive global AI community, multimodal annotation, enterprise delivery
  • 4
  • Turing
  • United States
  • Large expert network for advanced AI training and complex annotations
  • 5
  • TaskUs
  • United States
  • Enterprise-scale DAL, GenAI training, trust & safety
  • 6
  • Centific
  • United States
  • Enterprise annotation, multilingual and multimodal AI data
  • 7
  • DataForce by TransPerfect
  • United States
  • Global data collection and labelling, multilingual scale
  • 8
  • Invisible Technologies
  • United States
  • Expert human trainers for frontier GenAI systems
  • 9
  • iMerit
  • United States / India
  • Complex computer vision, autonomous systems, medical and GenAI data
  • 10
  • Sama
  • United States / East Africa

Managed annotation for computer vision and automotive AI

1. Scale AI

Why Scale AI ranked #1 in 2024

Scale AI had the strongest combination of market momentum, strategic relevance to frontier AI, enterprise relationships, and capital backing in 2024. In May 2024, the company raised $1 billion in a funding round that valued it at nearly $14 billion. Reuters reported that its customers included Microsoft, Morgan Stanley, OpenAI and Cohere, and described its core business as providing accurately labelled data used to train sophisticated AI systems. [1][2]

Scale also represented the direction in which the industry was moving: away from only basic labelling and toward higher-value human data for generative AI. A later Reuters report said Scale generated about $870 million in 2024 revenue, with substantial business coming from human trainers and complex datasets used to post-train advanced models. That scale of commercial activity, combined with its long-established presence in autonomous vehicles, enterprise AI and government work, gives it the strongest case for the top position in a worldwide 2024 ranking. [1][2]

Best suited for: frontier model developers, large enterprises, autonomous systems, and organisations needing sophisticated human-in-the-loop data at very high scale.

2. Appen

Why Appen ranked #2 in 2024

Appen remained one of the most established global data annotation companies in 2024. Everest Group placed Appen among the Leaders in its 2024 Data Annotation and Labeling Solutions for AI/ML PEAK Matrix, evaluating providers on market impact, vision and capability. Appen stated that it had more than 27 years of experience, a global crowd of more than one million contributors, and coverage across more than 235 languages. [3][4]

An April 2024 industry summary of Everest's assessment ranked Appen first among the traditional DAL providers and noted that Appen had completed more than 20,000 projects and handled billions of data units on its platform. Appen therefore earns the second position here: its global workforce, multilingual reach, mature platform and long enterprise track record were exceptional, although Scale AI's 2024 position in frontier-model training and market valuation gave Scale the edge in this broader editorial ranking. [3][4]

Best suited for: global multilingual projects, search and relevance data, speech, text, computer vision, LLM data, and enterprises requiring a mature large-crowd model.

3. TELUS International (now TELUS Digital)

Why TELUS ranked #3 in 2024

TELUS International was another clear 2024 leader in enterprise data annotation. Everest Group's 2024 assessment placed TELUS in the Leader category alongside Appen, Centific, TaskUs and Akkodis. The report described TELUS as having end-to-end AI data capabilities and a global delivery footprint covering North America, Europe and Asia. [5]

The same assessment reported more than 1.2 million gig workers on its crowdsourcing platform by the first half of 2023 and highlighted its Ground Truth Studios platform, annotation workbench tools, expert sourcing for GenAI, multimodal annotation, RLHF, prompt creation and model monitoring. TELUS ranks third because few providers combined such a large crowd with global enterprise operations and a broad multimodal technology stack. [5]

Best suited for: multilingual and multimodal enterprise programs, image/video/LiDAR, speech, text, sensor data, and managed GenAI data workflows.

4. Turing

Why Turing ranked #4 in 2024

Turing emerged in 2024 as a major supplier of expert human data for advanced AI models. Reuters reported in January 2025 that Turing's revenue had tripled to about $300 million during 2024 and that the company had reached profitability. It also said Turing had access to more than four million human experts, including software developers and doctorate-level scientists, who could be contracted to label and create data for AI models. [6]

Turing's strength was not necessarily the same type of commodity annotation associated with older crowdsourcing platforms. Its value was in complex, high-cost annotations and expert feedback for leading AI labs. Reuters reported that Turing listed OpenAI, Google, Anthropic and Meta as clients. It ranks fourth because its 2024 scale and frontier-AI relevance were extraordinary, while the companies above it had broader or more mature annotation-service footprints across traditional modalities. [6]

Best suited for: advanced LLM training, coding and STEM tasks, expert-labelled datasets, model evaluation, and projects where specialist knowledge matters more than low-cost microtasks.

5. TaskUs

Why TaskUs ranked #5 in 2024

TaskUs was recognised as a Leader in Everest Group's 2024 Data Annotation and Labeling assessment. Everest highlighted TaskUs for robust data collection capabilities and a strong focus on LLM and generative-AI training use cases such as prompt engineering, hallucination mitigation and adversarial testing. [7]

TaskUs also brought an enterprise-outsourcing operating model that is attractive for large programs requiring workforce management, security, trust and safety, and consistent service delivery. It ranks fifth because it combined large-scale operations with increasingly important GenAI services, although it was less singularly identified with AI training data than Scale, Appen, TELUS or Turing. [7]

Best suited for: enterprise DAL outsourcing, generative-AI evaluation, prompt work, adversarial testing, content safety, and programs requiring tightly managed operations.

6. Centific

Why Centific ranked #6 in 2024

Centific was one of the five Leaders in Everest Group's 2024 global DAL assessment. An industry review of the matrix placed Centific third among the traditional providers, noting capabilities in LLMs, computer vision, speech, search relevance, maps, augmented driving and AR/VR, together with experience in RLHF and AI red teaming. [4][5]

Centific's position reflects breadth rather than a single headline statistic. It had the enterprise delivery capabilities and multimodal coverage expected of a top-tier provider, while also adapting to the newer LLM-evaluation and safety requirements emerging in 2024. It ranks sixth in this broader list because the companies above it had stronger disclosed workforce, revenue, or market-position signals. [4][5]

Best suited for: enterprises needing multilingual and multimodal annotation across computer vision, maps, speech, search, LLM evaluation and responsible-AI workflows.

7. DataForce by TransPerfect

Why DataForce ranked #7 in 2024

DataForce benefits from TransPerfect's worldwide language-services infrastructure and is built specifically around AI data collection and labelling. DataForce describes itself as a worldwide data collection and labeling platform with a community of more than one million data contributors, scientists and engineers; TransPerfect operates in more than 140 cities worldwide. [8][9]

Its greatest advantage is global sourcing and multilingual reach. For projects involving speech, text, image, user studies, localisation or region-specific data collection, that footprint can be more important than having the most heavily marketed annotation platform. It ranks seventh because its global network is clearly large, while less public 2024 information is available about its share of frontier LLM post-training work compared with the companies ranked above it. [8][9]

Best suited for: multilingual data collection, speech and language AI, international image/text projects, user studies, and companies that need broad geographic coverage.

8. Invisible Technologies

Why Invisible Technologies ranked #8 in 2024

Invisible Technologies became one of the most important names in expert-led AI training during 2024. Reuters reported in September 2024 that Invisible had about 5,000 trainers across more than 100 countries, including PhD holders, master's degree holders and other knowledge-work specialists. Reuters also reported that Cohere and AI21 confirmed they were customers, while Invisible said it worked with Microsoft and OpenAI. [10]

Invisible's strength was the quality and specialisation of its human trainers rather than mass-market microtask annotation. It helped AI labs reduce hallucinations and perform higher-complexity post-training tasks. It ranks eighth because its 2024 impact on frontier GenAI was substantial, but its workforce was smaller and its traditional multimodal annotation footprint was narrower than the larger global DAL providers above it. [10]

Best suited for: frontier LLM post-training, expert evaluation, reasoning and domain-specific tasks, hallucination reduction, and high-complexity human feedback.

9. iMerit

Why iMerit ranked #9 in 2024

iMerit was recognised as a Major Contender in Everest Group's 2024 Data Annotation and Labeling assessment. The company has long focused on high-complexity annotation for areas such as computer vision, autonomous systems, geospatial AI, medical AI and, increasingly, generative AI. [5][11]

iMerit's appeal is its managed, expert-oriented approach. It is particularly credible when datasets require detailed instructions, 2D/3D geometry, LiDAR, segmentation, or domain expertise rather than simple high-volume tagging. It ranks ninth because Everest placed it below the Leader tier in 2024, yet its technical depth and specialised annotation capability still made it one of the strongest global providers. [5][11]

Best suited for: autonomous driving, geospatial AI, medical imaging, complex computer vision, LiDAR, and specialised data programs that need tightly managed quality.

10. Sama

Why Sama ranked #10 in 2024

Sama remained a recognised global annotation provider in 2024, especially in computer vision and automotive AI. Everest Group included Sama among the Major Contenders in its 2024 DAL assessment. In June 2024, Sama launched a scalable medium-length sequence annotation solution for automotive AI, combining human feedback with proprietary algorithms to improve sensor-data annotation workflows. [5][12]

Sama's managed workforce model and computer-vision expertise gave it a strong position for production annotation, particularly when quality and repeatability mattered. It ranks tenth because it had a narrower publicly visible 2024 footprint in frontier LLM training than several firms above it, but it remained a serious large-scale annotation partner with established delivery capabilities. [5][12]

Best suited for: automotive AI, computer vision, sensor data, image/video annotation, and managed production pipelines where consistency is critical.

Which company was best for different AI data needs in 2024?

Need Strong 2024 choices
Frontier LLM training and post-training Scale AI, Turing, Invisible Technologies
Very large multilingual crowd projects Appen, TELUS International, DataForce
Enterprise managed annotation TELUS International, TaskUs, Appen, Centific
Autonomous driving / LiDAR / computer vision Scale AI, TELUS International, iMerit, Sama
Expert coding, science, finance or reasoning data Turing, Invisible Technologies, Scale AI
Speech and language data across many markets Appen, TELUS International, DataForce, Centific

Sources and further reading

Frequently asked questions

There is no single audited measure that defines “largest.” By 2024 market valuation and frontier-AI prominence, Scale AI had the strongest claim in this list. Appen and TELUS International, however, operated much larger publicly disclosed global contributor communities, each above one million workers or contributors.

Appen, TELUS International and DataForce were especially strong choices because each operated large international contributor networks and supported multilingual data collection and annotation.

Scale AI, Turing and Invisible Technologies stood out for human-generated training data and expert feedback for advanced AI models. TaskUs, TELUS and Centific also offered GenAI-oriented services such as RLHF, prompt work, model evaluation or red teaming.

The terms are often used interchangeably. Data labelling usually means assigning tags or categories, while annotation can include richer information such as bounding boxes, segmentation masks, keypoints, relationships, transcriptions, rankings, explanations or human preference feedback.

Automation can accelerate straightforward annotation, but human validation remains important for ambiguous, safety-critical and expert tasks. Research published in 2024 found that automated annotation performance can vary considerably by task and should be validated against human-generated labels. [13]

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