Top 10 Data Annotation Companies to Outsource

For AI models to perform effectively the machines need to understand the data they process. Data annotation trains machines to understand the context. Partnering with trusted data annotation service providers ensures high quality data and effective AI models.

If your AI models are not performing at the optimum, time to first check the training data quality instead of algorithms. Most of the time the culprit is the data used to train the machines.

A study involving researchers from MIT and other institutions found an average error rate of at least 3.3% across 10 widely used machine learning datasets. Even a minute label inaccuracy can drastically impact the model performance.

That is the reason there is huge demand for high-quality labeled data to train, validate, and improve AI models. And that is the reason for the growth of the global data annotation market.

According to Grand View Research, the global data annotation tools market is projected to grow from approximately $1.0 billion in 2023 to $5.3 billion by 2030.

With the increase in AI adoption, outsourcing the data annotation needs to experts is the right approach apart from being cost-effective. This blog will help you pick the best suited partner for your needs.

We have compiled top 10 data annotation companies to outsource.

How we evaluated data annotation companies

There are hundreds of data annotation companies in the market, and we wanted to get you the most established and reputed ones. For that we followed a structured process and no company was picked randomly.

To ensure a fair and objective assessment, we evaluated the companies against a consistent set of criteria. We did a thorough research on publicly available information that included their websites, LinkedIn pages and other industry reports.

We measured each company against the following parameters.

• Industry experience: Longer market presence reflects on the ability to deliver and maturity to handle complex projects. We evaluated years of experience and market presence of each company.
• Annotation capabilities: Data annotation is broad and requires multiple capabilities to handle different kinds of data. A broader range enables service providers to support multiple AI use cases.
• Workforce strength: A larger and skilled workforce is needed to maintain turnaround times and quality standards.
• Business scale: We evaluated reported revenue, global presence, client base, and overall market standing to evaluate company’s operational stability.
• Technology and quality processes: Advanced annotation platforms, and quality-control processes play an important role in delivering accurate, consistent, and scalable training data.

Top 10 data annotation companies to outsource

Here are top 10 data annotation companies to outsource. Based on your project need, you may select one from the list.

1. Scale AI

Scale AI, founded in 2016 with its headquarters in San Francisco, California, specializes in providing high-quality training data for AI applications.

With company size of 501-1,000 employees their offerings range from image annotation services for self-driving cars, mapping, AR/VR, robotics, and more, to NLP, and classification models. With revenue of $870 million, Scale AI is known for its enterprise-grade infrastructure and scalable workforce model.

It supports technology companies, research teams, and global enterprises developing advanced AI systems. As for pricing they have not disclosed rates, and it is based on volume, complexity and scope.

Scale AI is best for building advanced AI systems that require large-scale, high-quality annotation for computer vision, autonomous vehicles, LiDAR, and generative AI applications.

2. Hitech BPO

Founded in 1992 and headquartered in Ahmedabad, India, Hitech BPO is a provider of data annotation and labeling services for AI and machine learning applications. Backed by a team of 300+ data annotators, the company supports image, video, text, audio, LiDAR, and geospatial annotation projects across multiple industries.

With more than 100 million data points annotated across 100+ data types, Hitech BPO reports up to 99.5% annotation accuracy and 95%+ inter-annotator agreement (IAA) accuracy through multi-level quality assurance processes. The company supports over 20 AI and machine learning industries, including healthcare, finance, agriculture, robotics, and retail.

Hitech BPO claims to help clients accelerate AI training by up to 40% while reducing annotation costs by up to 70% compared to in-house operations.

HitechBPO is best suited for organizations seeking scalable, end-to-end data annotation services across diverse AI and machine learning applications.

3. iMerit

Founded in 2012, and headquartered at San Jose, California, iMerit has a workforce of 10,000+ data specialists spanning 60+ countries across the globe. According to its LinkedIn profile, the company employs between 1,001 and 5,000 people directly.

They work with companies dealing in autonomous technology, geospatial technology, medical AI, and other such innovative industries. iMerit specialises in computer vision, healthcare and geospatial.

With $1.1 Billion revenue, iMerit has been recognized with many awards of excellence. They have made it to The Economic Times Cross-Border Business Growth Award ’26.

iMerit is best for complex data annotation for computer vision and advanced AI models required for healthcare, autonomous mobility, geospatial intelligence, and generative AI.

4. Appen

With headquarters in Kirkland, Washington, founded in 1996, Appen provides diverse datasets that power the world’s leading AI models. With a team strength of 501-1,000 employees. the company specializes in text, speech, NLP and multilingual AI.

With 1M+ vetted contributors worldwide, Appen has 20K+ completed projects, across 170+ countries. 80% of the world’s leading LLM builders are Appen customers, as reported on their website. ASX-listed (APX) the company operates globally across North America, Europe, Asia-Pacific, and the Philippines.

With a revenue of $273 million the company has completed 100 M LLM data elements and processed over 10B units of data.

Appen is best suited for large-scale text, search relevance, NLP, conversational AI, speech, and multilingual training data annotation.

5. TELUS Digital

Established in 2005, TELUS Digital has headquarters in Vancouver, British Columbia. The company supports the DX and CX needs of some of the world’s most established and/or disruptive brands.

With 10,001+ employees and revenue of $2.7 Billion, the company specializes in multilingual AI data, NLP and customer experience solutions.

The company specializes in multilingual annotation across hundreds of languages. The company delivers 50+ C support languages and 500+ data annotation languages to clients across 35+ countries. With 70+ global delivery centers and digital studios the company has strategic partnerships with leading global technology companies.

TELUS Digital is best for large-scale multilingual annotation across hundreds of languages, refining AI models for natural language processing.

6. Cogito Tech

Based in New York with a company strength of 1,001-5,000 employees, Cogito Tech was founded in 2014. The company specializes in computer vision, NLP and generative AI solutions with a listed revenue of $5.1 million.

The company caters to a diverse clientele, including sectors such as healthcare, finance, retail, and autonomous vehicles, ensuring compliance and security in their operations.

With more than 1500 data experts, the company provides established methodology through human-in-the-loop workforce solutions. The company further leverages regional expertise, language proficiency and regulatory compliance to support global initiatives.

The company is well equipped with safety regulations. In addition to GDPR, CCPA, and SOC2 Type 2 certifications, the delivery centers are compliant with international data security standards.

Cogito Tech is best for companies looking for cost-effective image, video, LiDAR, and medical image annotation services.

7. CloudFactory

Founded in 2010 in Nepal, CloudFactory provides a cloud workforce for machine learning and business data processing. Over the years the company built a global AI workforce that served over 700 clients launch groundbreaking AI solutions.

The company offers human-in-the-loop data annotation services that delivers accurate data enhancing AI model performance. With employee strength of 1,001-5,000, the company combines governance, human oversight and workflow into one integrated system.

The company partners with the client’s team to create AI solutions leveraging deep machine learning and artificial intelligence that aligns with client’s business goals. Reported revenue of the company is approximately $974.6 Million.

CloudFactory is best for businesses looking for human-in-the-loop data annotation services combined with scalable workflows and quality.

8. Labelbox

Founded in 2018 and based in San Francisco, California, Label box acts as a central hub for humans to interface with AI. It is a collaborative training data platform for computer vision machine learning applications.

With a team strength of 51-200 employees Labelbox provides fully managed data solutions across 40+ countries. With $189M funding to date the company partners with over 80% of leading AI labs in the US and the innovators defining the next frontier of AI.

With reported revenue of $21 million, the company provides data solutions across 40+ countries. The company can handle multilingual AI projects with 75+ language skills and 200+ professionals.

With SOC 2 Type II certified infrastructure, 1 M+ knowledge workers, 50k+ phDs and 200k+ master degrees the company ensures high quality annotation and data security.

Labelbox is best for enterprises that need a robust annotation platform to manage, annotate, and improve annotation throughout the AI development lifecycle.

9. Shaip

Founded in 2021, headquartered in New York, Shaip supports global organizations with structured AI data solutions. With a team strength of 201-500 employees the company focusses on building structured data to build reliable models.

With multidisciplinary teams across engineering, transcription, data science, research, and delivery operations, Shaip supports global organizations with structured AI data solutions. Shaip offers specialized solutions for multiple sectors and use cases including healthcare, e-Commerce, retail, BFSI, automotive, IT and telecom.

Shaip delivers datasets at scale with the support of 30,000+ vetted contributor community across 60+ countries and propriety ShaipCloud platform.

The reported revenue is approximately $146.2 million.

Shaip is best for healthcare, conversational AI and NLP solutions. The go to company for annotation of medical records, clinical datasets, speech data, and NLP training data.

10. Mindy Support

Mindy Support, located in Cyprus, is a global provider of data annotation and BPO services. Founded in 2013, they offer image and video annotation, as well as text and audio annotation.

With more than 2000 specialists and global offices in Cyprus, Ukraine, Poland, Bulgaria, India, Philippines, Egypt, Mindy Support services include data preparation, data annotation, data collection and quality assurance offerings.

The company has 1300+ projects the company helps clients with most advanced annotation challenges. The company serves wide array of industries including automative, agriculture, telecom, retail and robotic.

The company reports annual revenue of less than $5 million.

Mindy Support is best suited for companies looking for customised flexible image, video, text, and content moderation services.

Top 10 data annotation companies at a glance

Note: Revenue and workforce figures are based on publicly available information from company websites, LinkedIn profiles, ZoomInfo listings, and other business directories.

How to select the right data annotation company

To get the right data for your annotation project you need to connect with the right partner. Your chosen partner must align with your project goals. Here are some key points to keep in mind while selecting data annotation company.

• Identify your AI use case: Identify your requirement and then look for companies with experience in the relevant domain.
• Assess annotation capabilities: The provider must have expertise in the type of data you need annotated and the annotation techniques required for the project
• Look for domain expertise: Check whether the provider has domain experts and experience handling datasets like yours especially for projects like medical imaging, autonomous vehicle, finance among others.
• Assess scalability: If your project requirement fluctuates or involves seasonal spikes choose a company with scalable workforce.
• Ensure quality control: Choose providers that use multi-level reviews, human-in-the-loop validation, and quality metrics such as IAA to maintain annotation accuracy and consistency.
• Check technology: Choose a company that stays updated with AI-assisted annotation tools and advanced platforms to improve efficiency, consistency, and turnaround times.
• Verify compliance and data security: For sensitive datasets, ensure the provider follows security standards such as ISO 27001, GDPR, HIPAA, or other relevant compliance frameworks.
• Market credibility: Choose a company that has market credibility with good reviews.
• Compare pricing: Though pricing depends on multiple factors like project complexity and volume, still do a market survey.
• Ask for pilot project: Before committing to a large engagement, run a pilot to evaluate annotation quality, responsiveness, and adherence to project guidelines.

Conclusion

The success of any AI model depends on the quality of data used to train it. That is the reason the demand of for high-quality training data and specialized annotation expertise is increasing.

With inaccurate labelled data, AI models often struggle to make sense of the information. The machines need to interpret and understand the context. There is an urgent need for quality annotation for the models to learn effectively and perform.

It is always a good idea to get the annotation done by the experts with skilled talent, proven quality-control processes, and the latest annotation tools and technologies.

We have listed top 10 data annotation companies to help you identify the right partner for your needs. Based on your project needs you may opt the one best suited.

FAQs

• What is a data annotation company?
  • A data annotation company is a service provider that labels raw data of any form like text, audio, video or image. This helps machine understand the context and interpret the data better for an effective AI model.

    These companies are fully equipped with trained annotators, domain experts and with the latest tools and technology. They help companies improve model accuracy.

• How do I choose the right data annotation company?
  • Identify your project requirement and then match with the offerings of service provider to select the one suited to your needs.

    Check the providers industry experience, evaluate annotation capabilities and domain knowledge. Review their workforce strength and technology and tools available with them. Also evaluate the financial stability of the company and check the reviews for their market credibility.

• Should I outsource data annotation or build an in-house team?
  • Outsourcing is always a good idea as you get access to expert team, best of technology, domain experts and established quality control process. It also proves cost-effective as the service providers can easily scale as per your needs.

    Though inhouse team often provides better control it can be beneficial when annotation is a long-term, core business requirement.

• How much does data annotation cost?
  • Data annotation costing depends on the complexity of the project, volume and turnaround time. Simple image or text annotation may cost less compared to complex ones like video annotation or LiDAR labelling.

    Some charge on per image basis while some may charge on projects basis. For specialized domains like medical or geospatial you may be charged more.

• What types of data can be annotated?
  • Any kind of data that is needed to train machine learning models can be annotated. It could be image, text, video or audio data.

    LiDAR and 3D Data for autonomous vehicles, medical data for annotation of medical images, geospatial data for satellite imagery can also be annotated.

    Multimodal data where a combination of all types of data is used for generative AI applications can also be annotated.

• How do data annotation companies ensure quality?
  • Data annotations companies maintain quality through a structured workflow and trained annotators. The ensure quality through a multi-level reviews, audits and technology assisted validation.

    And most important their workflows always include human-in-loop verification for edge cases and prevent quality lapse. Human reviews also take care of any ambiguity in data understanding by the machines.

• What compliance certifications should a data annotation company have?
  • The compliance certificate needed for any annotation company depends on the type of data being annotated and the industry being served.

    But a minimum of ISO 27001 that ensures that the company follows internationally recognized practices must be in place. Also, other compliances like SOC 2, GDPR, HIPAA, CCPA, NDA and any industry specific compliance must be in place.

• Can I run a free pilot before committing to a project?
  • Yes, you can. In fact, you must ask for a pilot project before you sign the dotted line. A pilot project helps you assess the quality, turnaround time and adherence to project guidelines.

    Many providers offer this option and in fact many even provide them free of cost. It helps evaluate the service provider.