Data Annotation & Labeling Market by Offering, Data Form, Deployment Type, Data Type, Technology, Organization Size, Application, End-User

List of Tables

The Data Annotation & Labeling Market was valued at USD 1.90 billion in 2024 and is projected to grow to USD 2.37 billion in 2025, with a CAGR of 26.13%, reaching USD 7.67 billion by 2030.

KEY MARKET STATISTICS
Base Year [2024]	USD 1.90 billion
Estimated Year [2025]	USD 2.37 billion
Forecast Year [2030]	USD 7.67 billion
CAGR (%)	26.13%

The data annotation and labeling market has evolved into a critical component of today's digital ecosystem. In an era where machine learning and artificial intelligence applications play a pivotal role in driving innovation, the accuracy and quality of annotated data determine the success of these technologies. As industries increasingly rely on automated systems, the need for precise, well-labeled datasets has never been greater. This introductory section explores the essential role of data annotation in powering advanced analytics, enabling smarter decision-making, and enhancing both operational efficiency and competitive positioning.

A surge in digital transformation has spurred the adoption of data-driven strategies across varied sectors such as healthcare, finance, retail, and manufacturing. Businesses are now investing heavily in systems that not only capture but also process and label data accurately. This evolution has been accompanied by the rise of sophisticated methodologies, a blend of human expertise and machine learning algorithms, and a robust market response that has accelerated innovation and efficiency. The landscape is continuously shifting towards higher accuracy, scalability, and cost-effective annotation models, making it an imperative discussion point for industry leaders and decision-makers looking to harness the full potential of their data systems.

Furthermore, technological advancements have pushed the boundaries of what is possible in the realm of data annotation. As organizations transition from conventional manual processes to more integrated and automated approaches, they are witnessing substantial improvements in both speed and precision. The consolidation of these emerging trends underscores the importance of understanding the underlying forces that are shaping the data annotation and labeling market. With a near-future that promises even greater integration of AI and human skill, this market is poised for transformative growth, making it a subject deserving of in-depth analysis and strategic foresight.

Transformative Shifts Driving Evolution in the Data Annotation Landscape

Over the past few years, the data annotation market has experienced a series of transformative shifts that are redefining traditional processes and operational frameworks. Rapid advancements in artificial intelligence and machine learning have moved the industry from manual, time-consuming tasks towards more sophisticated, automated methodologies. These shifts are further amplified by the growing need to manage increasingly complex datasets in a variety of formats. Technological innovation, evolving customer demands, and competitive pressures have all contributed to reshaping the landscape.

The integration of AI-driven solutions has revolutionized the manner in which data is annotated, blending efficiency with high accuracy. Companies are now leveraging automation to reduce the time and resources traditionally required for data labeling, while still ensuring that the quality standards meet or exceed those of conventional methods. Consequently, leaders in the field are investing in hybrid systems that balance advanced computational techniques with the irreplaceable nuance of human judgment. This development is crucial in applications where even slight inaccuracies can lead to significant errors in subsequent analysis or operational performance.

Other transformative factors include the globalization of data sources and the prevalence of multi-format information. Business models have adapted to recognize that data comes in numerous forms and from a diverse range of sources, necessitating a more dynamic, versatile approach to annotation. As companies expand their digital footprints, the ability to scale annotation processes without compromising on quality becomes a significant competitive advantage. In this scenario, the deployment of innovative, scalable technologies coupled with strategic partnerships is propelling the market forward at an unprecedented pace, ensuring that businesses are well-equipped to navigate the rapidly evolving digital terrain.

Key Segmentation Insights in the Data Annotation & Labeling Market

The segmentation of the data annotation market provides crucial insights into emerging trends and tailored solutions across various dimensions. Analysis by offering reveals that the market is divided into services and solutions, each addressing specific operational needs and technological challenges. This segmentation enables stakeholders to understand how companies are deploying distinct approaches to cater to diverse industry requirements. Whether it is through comprehensive service packages or specialized solution platforms, the focus remains on enhancing the overall efficiency of data processing.

Further disaggregation by data form illustrates a comprehensive study across audio, image, text, and video formats. The incorporation of these varied forms into data annotation pipelines ensures that even the most complex datasets receive the detailed attention they require. With an increasing reliance on video content and audio analytics for applications ranging from security to customer service, the capability to process multiple data forms has become indispensable. This evolution is driving market growth as businesses across sectors strive to integrate diverse data types into coherent, actionable insights.

When considering deployment type, the analytical lens shifts to cloud-based versus on-premise solutions. The flexibility and scalability provided by cloud implementations are leading to wider adoption in enterprises of all sizes, whereas on-premise solutions continue to serve sectors that require heightened data security or customized deployment. As each organization navigates its digital transformation journey, the decision between these deployment models often reflects broader strategic imperatives concerning cost, control, and performance.

Data type segmentation further differentiates the market into semi-structured, structured, and unstructured data. This categorization underlines a nuanced understanding of data formats and the corresponding annotation techniques necessary to manage them effectively. The increasing utilization of unstructured data-ranging from social media feeds to sensor outputs-necessitates advanced annotation methodologies, thereby driving innovation in this segment.

Examining the market from a technology perspective highlights the trend towards automated or AI-driven annotation, hybrid systems, and traditional manual annotation. The advent of automated systems has dramatically reduced error margins and processing times, while hybrid approaches skillfully combine the best aspects of technology with human insight to cover edge cases and complex data scenarios. This diversification ensures that the market can cater to a wide spectrum of use cases, from fully automated cloud-based analyses to intricately managed manual processes.

Additional segmentation by organization size into large enterprises versus small and medium enterprises (SMEs) emphasizes the differentiated needs of various business scales. Large enterprises often rely on extensive, scalable solutions and have the resources to invest in cutting-edge technology, while SMEs typically seek agile, cost-effective solutions that provide quick returns on investment without extensive infrastructure overhead.

Application-based segmentation further refines market insights with a focus on catalog management, content management, data quality control, dataset management, security and compliance, sentiment analysis, and workforce management. This breadth of applications is indicative of the vast potential for annotated data to drive performance in domains as diverse as customer relationship management to operational security. Each application area demands tailored solutions and expertise, reflecting the complex interplay between technological capability and business requirements.

Considering the end-user perspective, the market is segmented across sectors as varied as aerospace and defense, automotive and transportation, banking, financial services and insurance, energy, government and defense, healthcare, IT and telecommunication, manufacturing, and retail. This end-user segmentation demonstrates the extensive applicability of data annotation across industries, highlighting how technology and data converge to streamline operations and deliver measurable value across both public and private sectors.

Based on Offering, market is studied across Services and Solution.

Based on Data Form, market is studied across Audio, Image, Text, and Video.

Based on Deployment Type, market is studied across Cloud and On-Premise.

Based on Data Type, market is studied across Semi-structured Data, Structured Data, and Unstructured Data.

Based on Technology, market is studied across Automated/AI-driven Annotation, Hybrid Systems, and Manual Annotation.

Based on Organization Size, market is studied across Large Enterprise and Small & Medium Enterprise.

Based on Application, market is studied across Catalog Management, Content Management, Data Quality Control, Dataset Management, Security & Compliance, Sentiment Analysis, and Workforce Management.

Based on End-User, market is studied across Aerospace & Defense, Automotive & Transportation, Banking, Financial Services & Insurance, Energy, Government & Defense, Healthcare, IT & Telecommunication, Manufacturing, and Retail.

Key Regional Insights Shaping Market Dynamics

A regional analysis of the data annotation and labeling market reveals distinct trends that vary across different parts of the globe. In the Americas, market players have been quick to adopt innovative data annotation techniques driven by robust technological infrastructures and significant investments in artificial intelligence. The well-established ecosystem in this region is characterized by rapid technological advancements coupled with a focused drive towards digital optimization, making it a hotspot for emerging solutions that cater to sophisticated data processing needs.

In the combined region of Europe, Middle East & Africa, the market is witnessing a fusion of varied technological strategies influenced by diverse regulatory environments and unique industry requirements. European markets are often at the forefront of digital compliance and security, while the Middle East and Africa have carved out niches in specific sectors such as energy and telecommunications. The integration of high-tech solutions with localized understanding in these regions drives targeted applications that meet both global standards and regional needs.

The Asia-Pacific region stands out due to its rapid adoption of digital technologies and a growing emphasis on automation. Emerging economies in this region are capitalizing on the scalability and flexibility of cloud-based annotation solutions while simultaneously investing in state-of-the-art AI and machine learning capabilities. The dynamic mix of traditional industry practices and modern technological advancements in Asia-Pacific provides a unique perspective on the balance of cost efficiency and innovation in data annotation practices.

Based on Region, market is studied across Americas, Asia-Pacific, and Europe, Middle East & Africa. The Americas is further studied across Argentina, Brazil, Canada, Mexico, and United States. The United States is further studied across California, Florida, Illinois, New York, Ohio, Pennsylvania, and Texas. The Asia-Pacific is further studied across Australia, China, India, Indonesia, Japan, Malaysia, Philippines, Singapore, South Korea, Taiwan, Thailand, and Vietnam. The Europe, Middle East & Africa is further studied across Denmark, Egypt, Finland, France, Germany, Israel, Italy, Netherlands, Nigeria, Norway, Poland, Qatar, Russia, Saudi Arabia, South Africa, Spain, Sweden, Switzerland, Turkey, United Arab Emirates, and United Kingdom.

Key Companies Driving Innovation in the Market

A diverse group of leading companies is shaping the data annotation and labeling market by pushing the boundaries of what is possible with technology-driven solutions. Prestigious firms such as Adobe Inc. have set high standards through their versatile and integrated digital platforms, while innovators like AI Data Innovations and AI Workspace Solutions are spearheading the development of cutting-edge annotation methodologies. The market is further enriched by dynamic players like Alegion AI, Inc. by SanctifAI Inc. and Amazon Web Services, Inc., offering a continuum of services that range from cloud-based solutions to specialized, industry-specific applications.

The competitive landscape features companies like Annotation Labs and Anolytics, who complement the market by integrating advanced analytical tools with meticulous data processing capabilities. Established firms such as Appen Limited, BigML, Inc., and CapeStart Inc. continue to drive market expansion through robust service delivery and comprehensive technological solutions. These organizations, alongside Capgemini SE and CloudFactory International Limited, have contributed to the evolution of scalable platforms that address burgeoning data demands across a variety of formats and applications.

The market's vibrancy is further reinforced by the contributions of Cogito Tech LLC and Content Whale, whose innovative approaches are significantly influencing annotation practices. Firms such as Dataloop Ltd, Datasaur, Inc., and Deepen AI, Inc. are continuously improving accuracy and throughput by harnessing the potentials of AI-driven annotation systems. In a similar vein, DefinedCrowd Corporation and Hive AI add their specialized expertise, contributing to enhanced operational efficiencies and improved quality standards within the market.

Another notable segment of market leaders includes companies such as iMerit and International Business Machines Corporation, who have long been frontrunners in developing technology that meets the demands of modern data environments. Entities like KILI TECHNOLOGY SAS and Labelbox, Inc. have achieved significant milestones by offering sophisticated annotation platforms that seamlessly integrate with existing data workflows. Additionally, Learning Spiral, LXT AI Inc. alongside Oracle Corporation provide deep technological insights and robust frameworks that underpin many innovative solutions currently in the market.

Other influential players such as Precise BPO Solution, Samasource Impact Sourcing, Inc, Scale AI, Inc., Snorkel AI, Inc., and SuperAnnotate AI, Inc. are continuously refining the processes associated with data labeling to cater to a wide range of industry requirements. The competitive influence of TELUS Communications Inc., Uber Technologies Inc., and V7 Ltd. further highlights the dynamic nature of the market, where ongoing innovation and strategic collaborations drive both quality and efficiency. Collectively, these organizations reinforce the robust and continuously evolving nature of the data annotation and labeling industry, staying ahead of market demands and technological advancements.

The report delves into recent significant developments in the Data Annotation & Labeling Market, highlighting leading vendors and their innovative profiles. These include Adobe Inc., AI Data Innovations, AI Workspace Solutions, Alegion AI, Inc. by SanctifAI Inc., Amazon Web Services, Inc., Annotation Labs, Anolytics, Appen Limited, BigML, Inc., CapeStart Inc., Capgemini SE, CloudFactory International Limited, Cogito Tech LLC, Content Whale, Dataloop Ltd, Datasaur, Inc., Deepen AI, Inc., DefinedCrowd Corporation, Hive AI, iMerit, International Business Machines Corporation, KILI TECHNOLOGY SAS, Labelbox, Inc., Learning Spiral, LXT AI Inc., Oracle Corporation, Precise BPO Solution, Samasource Impact Sourcing, Inc, Scale AI, Inc., Snorkel AI, Inc., SuperAnnotate AI, Inc., TELUS Communications Inc., Uber Technologies Inc., and V7 Ltd.. Actionable Recommendations for Industry Leaders

For industry leaders seeking to capitalize on the emerging trends in the data annotation and labeling market, several strategic recommendations can be drawn from current insights. First, investing in and adopting hybrid models that integrate both automated and manual annotation processes will be crucial. These systems not only enhance accuracy but also provide the flexibility needed to adapt to various data challenges. Leaders should consider establishing robust partnerships with tech innovators and service providers to leverage cutting-edge technologies that streamline data management and annotation.

It is imperative to maintain agility in technology adoption. Companies should prioritize scalable cloud-based solutions alongside traditional on-premise options, aligning deployments with their specific operational needs and security requirements. This strategic balance can provide the dual benefit of high performance and stringent data control, making it easier to navigate regulatory environments and market-specific challenges. Furthermore, a focus on continuous training and skill development for personnel involved in annotation processes is essential to bridge the gap between evolving technology and practical application.

Given the highly segmented nature of the market, understanding the unique needs of different customer groups-from large enterprises to small and medium-sized organizations-will enable leaders to design and market solutions that address diverse pain points effectively. Tailoring services to specific applications, such as catalog management, data quality control, or security and compliance, can enhance customer satisfaction and drive adoption.

In addition, integrating state-of-the-art data quality controls and adopting AI-driven error correction mechanisms will help mitigate risks associated with mislabeling and ensure high data integrity. Decision-makers should actively pursue innovations that allow for real-time quality assessments and iterative improvements in annotation accuracy. Leveraging data across multiple formats-audio, image, text, and video-further reinforces the importance of a comprehensive, technology-agnostic approach that addresses the growing complexity of data landscapes.

Finally, fostering a culture of continuous innovation by investing in research and development is vital. Industry leaders should be proactive in monitoring market trends and technological breakthroughs, adapting business models accordingly to stay ahead of the curve. By doing so, not only will they improve internal operational efficiencies, but they will also position themselves as thought leaders in an increasingly competitive market.

Conclusion: Navigating Future Opportunities in Data Annotation

As the data annotation and labeling market continues its rapid evolution, it remains clear that the strategic integration of technological innovation with human expertise is driving this dynamic landscape. The convergence of advanced AI methodologies with scalable cloud and hybrid systems is fostering a robust foundation for future growth. Throughout this analysis, the importance of segmenting the market along various dimensions-from offerings and data forms to deployment types and end-user industries-has been underscored as crucial for understanding both current trends and future opportunities.

The transformative shifts observed, especially those driven by automation and real-time data processing, not only enhance operational efficiencies but also bolster overall data quality and integrity. The competitive influence of leading companies, combined with targeted regional strategies, strengthens the market's position and paves the way for further innovation. Decision-makers are now equipped with a clearer understanding of how to harness these trends to drive strategic growth and maintain a competitive edge in a digital-first environment.

Overall, the path forward is one of continuous learning and adaptation. Embracing new technologies, refining existing processes, and making informed strategic decisions will be central to capitalizing on the vast potential that the data annotation and labeling market offers. This comprehensive overview serves as both a roadmap and a call for proactive engagement, empowering companies to navigate the future with confidence.

Product Code: MRR-6723692285B0

1. Preface

1.1. Objectives of the Study
1.2. Market Segmentation & Coverage
1.3. Years Considered for the Study
1.4. Currency & Pricing
1.5. Language
1.6. Stakeholders

2. Research Methodology

2.1. Define: Research Objective
2.2. Determine: Research Design
2.3. Prepare: Research Instrument
2.4. Collect: Data Source
2.5. Analyze: Data Interpretation
2.6. Formulate: Data Verification
2.7. Publish: Research Report
2.8. Repeat: Report Update

3. Executive Summary

4. Market Overview

5. Market Insights

5.1. Market Dynamics
- 5.1.1. Drivers
  - 5.1.1.1. Rising demand for high-quality labeled data to enhance AI model performance
  - 5.1.1.2. Growing adoption of autonomous systems and computer vision applications
  - 5.1.1.3. Increasing government initiatives and investments in AI and machine learning
- 5.1.2. Restraints
  - 5.1.2.1. High costs and time-intensive nature of manual data annotation
- 5.1.3. Opportunities
  - 5.1.3.1. Integration of AI-assisted annotation tools to improve efficiency
  - 5.1.3.2. Expanding applications of data labeling in healthcare and life sciences
- 5.1.4. Challenges
  - 5.1.4.1. Ensuring data privacy and security in large-scale annotation projects
5.2. Market Segmentation Analysis
- 5.2.1. Offering: Shifting consumer preference towards adopting technologically advanced platforms to enhance efficiency and scalability
- 5.2.2. Data Form: Growing use of image data form in computer vision, facial recognition, and autonomous driving systems
- 5.2.3. Deployment Type: Consumers favor on-premise solutions due to privacy concerns
- 5.2.4. Data Type: Increasing usage of structured data as it is highly organized and easily searchable
- 5.2.5. Technology: Automated/AI-driven annotation adopted by organizations owing to its scalability, speed, and cost efficiency
- 5.2.6. Organization Size: Rapid expansion of small & medium enterprises accelerated demand for data annotation labeling
- 5.2.7. Application: Emergence of workforce management applications as vital support systems to optimize human resources in data annotation processes
- 5.2.8. End-User: Automotive & transportation sector prioritizing the integration of annotated data for the development of autonomous systems
5.3. Porter's Five Forces Analysis
- 5.3.1. Threat of New Entrants
- 5.3.2. Threat of Substitutes
- 5.3.3. Bargaining Power of Customers
- 5.3.4. Bargaining Power of Suppliers
- 5.3.5. Industry Rivalry
5.4. PESTLE Analysis
- 5.4.1. Political
- 5.4.2. Economic
- 5.4.3. Social
- 5.4.4. Technological
- 5.4.5. Legal
- 5.4.6. Environmental

6. Data Annotation & Labeling Market, by Offering

6.1. Introduction
6.2. Services
6.3. Solution

7. Data Annotation & Labeling Market, by Data Form

7.1. Introduction
7.2. Audio
7.3. Image
7.4. Text
7.5. Video

8. Data Annotation & Labeling Market, by Deployment Type

8.1. Introduction
8.2. Cloud
8.3. On-Premise

9. Data Annotation & Labeling Market, by Data Type

9.1. Introduction
9.2. Semi-structured Data
9.3. Structured Data
9.4. Unstructured Data

10. Data Annotation & Labeling Market, by Technology

10.1. Introduction
10.2. Automated/AI-driven Annotation
10.3. Hybrid Systems
10.4. Manual Annotation

11. Data Annotation & Labeling Market, by Organization Size

11.1. Introduction
11.2. Large Enterprise
11.3. Small & Medium Enterprise

12. Data Annotation & Labeling Market, by Application

12.1. Introduction
12.2. Catalog Management
12.3. Content Management
12.4. Data Quality Control
12.5. Dataset Management
12.6. Security & Compliance
12.7. Sentiment Analysis
12.8. Workforce Management

13. Data Annotation & Labeling Market, by End-User

13.1. Introduction
13.2. Aerospace & Defense
13.3. Automotive & Transportation
13.4. Banking, Financial Services & Insurance
13.5. Energy
13.6. Government & Defense
13.7. Healthcare
13.8. IT & Telecommunication
13.9. Manufacturing
13.10. Retail

14. Americas Data Annotation & Labeling Market

14.1. Introduction
14.2. Argentina
14.3. Brazil
14.4. Canada
14.5. Mexico
14.6. United States

15. Asia-Pacific Data Annotation & Labeling Market

15.1. Introduction
15.2. Australia
15.3. China
15.4. India
15.5. Indonesia
15.6. Japan
15.7. Malaysia
15.8. Philippines
15.9. Singapore
15.10. South Korea
15.11. Taiwan
15.12. Thailand
15.13. Vietnam

16. Europe, Middle East & Africa Data Annotation & Labeling Market

16.1. Introduction
16.2. Denmark
16.3. Egypt
16.4. Finland
16.5. France
16.6. Germany
16.7. Israel
16.8. Italy
16.9. Netherlands
16.10. Nigeria
16.11. Norway
16.12. Poland
16.13. Qatar
16.14. Russia
16.15. Saudi Arabia
16.16. South Africa
16.17. Spain
16.18. Sweden
16.19. Switzerland
16.20. Turkey
16.21. United Arab Emirates
16.22. United Kingdom

17. Competitive Landscape

17.1. Market Share Analysis, 2024
17.2. FPNV Positioning Matrix, 2024
17.3. Competitive Scenario Analysis
- 17.3.1. Palantir and Enabled Intelligence partner for integrated data annotation to drive efficient, intelligent AI model development in defense and intelligence settings
- 17.3.2. Crimson Phoenix strengthens its market leadership with the Blackspoke acquisition to drive next-generation AI-powered data annotation and geospatial intelligence solutions for the intelligence community
- 17.3.3. Toronto-based LXT enhances its market presence by acquiring German clickworker and integrating 6 million crowdworkers along with 500 enterprise clients to double revenues and transition into a technology-focused leader
- 17.3.4. datma launches an innovative automated pathology image labeling system within datma.FED to streamline data annotation, enhance privacy-first sharing, and accelerate precision medicine drug discovery advancements
- 17.3.5. Datma launches patent-protected automated pathology data labeling in datma.FED to streamline complex image integration and empower precision medicine research collaborations
- 17.3.6. Superb AI harnesses USD 10.2M funding to revolutionize AI development with innovative data labeling solutions
- 17.3.7. Protege partners with Centaur Labs to integrate expert multimodal medical data annotation for enhanced healthcare AI reliability and innovation
- 17.3.8. CNTXT partners with AI71 to drive the UAE's AI transformation through advanced data annotation, labeling, and unified platform services supporting Falcon AI model development and broad digital innovation initiatives
- 17.3.9. NGA launches $700M data labeling competition and training initiative to elevate geospatial intelligence and ethical AI accreditation in military and commercial sectors
- 17.3.10. The National Geospatial-Intelligence Agency's USD 700M Investment in Data Labeling to Propel AI-Powered Geospatial Intelligence
- 17.3.11. Sapien AI and Yield Guild Games merge gamified data labeling with onchain gaming to build a global blockchain network that enhances AI training data quality and drives engagement in the evolving tech landscape
- 17.3.12. Sama launches a scalable medium-to-long sequence frame annotation solution that integrates human insight with automated intelligence, delivering up to 10 million shapes per day at 99% quality for leading automotive OEMs and Tier 1 suppliers while significantly reducing development costs and model risks
- 17.3.13. Scale AI secures $1B funding to drive rapid innovation, expansive global growth, and long-term leadership in data annotation and labeling services
- 17.3.14. Strategic partnership between V7 and Aya Data enhances high-precision, 10x faster data labeling for complex AI projects while driving global market expansion and technological innovation
- 17.3.15. Sapien AI Corp secures $5 million in seed funding led by Animoca Ventures to revolutionize data annotation with a blockchain-based, gamified platform engaging over 1 million multilingual labelers for AI training in healthcare and education
- 17.3.16. Uber strengthens its AI edge by launching a dedicated in-house data labeling division to streamline operations and accelerate market leadership in the autonomous and analytical tech sectors in Asia
- 17.3.17. Strategic alliance between V7 and Aya Data accelerates AI development with enhanced data annotation solutions
17.4. Strategy Analysis & Recommendation
- 17.4.1. iMerit
- 17.4.2. Scale AI, Inc.
- 17.4.3. CloudFactory International Limited
- 17.4.4. Appen Ltd.

Companies Mentioned

1. Adobe Inc.
2. AI Data Innovations
3. AI Workspace Solutions
4. Alegion AI, Inc. by SanctifAI Inc.
5. Amazon Web Services, Inc.
6. Annotation Labs
7. Anolytics
8. Appen Limited
9. BigML, Inc.
10. CapeStart Inc.
11. Capgemini SE
12. CloudFactory International Limited
13. Cogito Tech LLC
14. Content Whale
15. Dataloop Ltd
16. Datasaur, Inc.
17. Deepen AI, Inc.
18. DefinedCrowd Corporation
19. Hive AI
20. iMerit
21. International Business Machines Corporation
22. KILI TECHNOLOGY SAS
23. Labelbox, Inc.
24. Learning Spiral
25. LXT AI Inc.
26. Oracle Corporation
27. Precise BPO Solution
28. Samasource Impact Sourcing, Inc
29. Scale AI, Inc.
30. Snorkel AI, Inc.
31. SuperAnnotate AI, Inc.
32. TELUS Communications Inc.
33. Uber Technologies Inc.
34. V7 Ltd.