Outsourced data annotation services: List of best companies to work for

Outsourced data annotation services are a smart way to scale AI solutions efficiently. By leveraging professional data annotation services, businesses can save costs while ensuring quality and data security. In this article, DIGI-TEXX provides a List of data annotation companies to help you choose the best provider for your business project.

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Why Data Annotation Is Critical for AI Success

Professional data annotation services are the core foundation when implementing projects applying Artificial Intelligence (AI) and Machine Learning. It can be seen that, in the digital era, data is an important asset, but raw data itself is worthless if not processed well. This is where the role of data annotation becomes important.

A specific example, when training an AI model to detect tumors in medical X-ray images. Without proper annotation (localization and labeling of ‘tumor’ or ‘normal’), the AI ​​model will not be able to distinguish between healthy tissue and signs of disease. Similarly, a self-driving car needs pixel-by-pixel labeled video data (semantic segmentation) to differentiate between ‘roads’, ‘pedestrians’, ‘other vehicles’ and ‘obstacles’.

In order for AI models to make accurate decisions, they need to be trained on a huge dataset that has been carefully, accurately and consistently labeled. The quality of the ground truth will directly affect the performance, reliability and fairness of the AI ​​model. A poorly annotated dataset will lead to a poorly performing AI model, causing wasted resources and even serious real-world consequences.

What Are Data Annotation Services?

Data annotation services are specialized services, often provided by third-party companies, that focus on performing the process of labeling, marking, classifying and enriching raw data. The goal of this process is to turn unstructured data such as images, videos, text, and audio into structured data that Machine Learning algorithms can use to learn and predict specific information.

In general, this process requires a combination of human effort combined with specialized software tools and a quality management process to ensure accuracy and consistency at scale.

The types of data annotation services vary widely, depending on the type of data and the goals of the AI ​​project. Here are the most common types:

• Image & Video Annotation: This is an important area, meaning it plays a role for Computer Vision models. Services of this type include identifying and labeling objects in images or videos, drawing simple bounding boxes around vehicles, and complex pixel-level segmentation techniques for medical applications or self-driving cars.
• Text Annotation: Text Annotation is the potentially key for Natural Language Processing (NLP) models. This process helps computers understand the meaning, context, and sentiment of written language. Common tasks include extracting important information fields (such as names of people, places), analyzing sentiments of comments, or classifying existing text according to specific topics.
• Audio Annotation: Is the foundation for today’s popular speech recognition systems. This process focuses on converting speech to text, identifying distinct sound patterns (like car horns, glass breaking) or distinguishing different voices within the same conversation.

It is clear that performing these tasks at scale requires a large, well-trained team and a rigorous quality assurance (QA/QC) process. This is why many organizations choose to outsource data labeling rather than build their own internal team.

Why Outsource Data Annotation?

Currently, when AI projects are deployed, companies often let their engineers or data scientists do the labeling themselves. However, when the project scales from a few hundred to several million data points, this model quickly becomes ineffective. Building an in-house labeling team is extremely expensive, difficult to manage, takes time to train, and most importantly, diverts the focus of senior technical resources from the core competencies of that team. Here are some of the key reasons to use a third-party service

Save time and money.

This is the most obvious and easily measurable benefit. The company will not need to recruit, train, pay salaries, benefits, and manage a large team of annotators. This cost is especially high in markets like North America and Western Europe. By partnering with an offshore annotation company in a cost-competitive location, businesses can save 40% to 70% on operating costs.

Businesses also don’t need to invest in expensive specialized annotation software licenses, develop in-house tools, and train staff. Professional data annotation service providers already have this entire technology infrastructure in place so they can get started right away.

Higher quality thanks to a professional team

Businesses will rely on the expertise and experience of data annotation service companies. They always have highly trained teams, experienced in handling millions of data points, and understand complex cases.

In addition, when outsourcing, the Quality Assurance (QA) process is completely organized. For example, data can be annotated by one person, then reviewed by a quality manager, and even randomly checked by senior management. They use the IoU (Intersection over Union) metric to measure accuracy.

>>> See more: Understanding the 6 Key Data Quality Dimensions

Flexible scaling

Imagine a business that needs 10,000 annotated images this month, but 500,000 images next month when the model begins its deep training phase, and then pauses for a few weeks to analyze the results. Increasing productivity would be difficult for in-house teams.

Outsourcing data labeling gives businesses the flexibility to scale up or down their team almost instantly. If they need to handle a spike in workload, the service provider can add hundreds of people to the project. If the project pauses, you don’t have to worry about maintaining an idle team.

Otherwise, an AI project might start with image annotation, but then expand to video or audio annotation. Instead of having to retrain your entire internal team, a comprehensive data annotation services partner has dedicated teams for each type of data, ready to respond immediately.

Ensure data security.

AI training data often includes a variety of sensitive data. This could be patient medical records (HIPAA compliant), personal financial data (PCI), facial recognition, or security camera video containing personally identifiable information (PII). Professional service providers will invest heavily in security. They always ensure compliance with top security certifications such as ISO 27001 and GDPR.

In addition, most service providers will use strict access controls in the office (secure, mobile-free work areas), data encryption (both at rest and in transit), use virtual private networks (VPNs), and secure, isolated annotation platforms.

List of data annotation companies in the USA 2026

Here’s a curated list of Data labelling companies for 2026, providing high-quality training data for AI across image, text, audio, and video annotation.

1. DIGI-TEXX

Best for:

Companies that need outsourced data annotation services to handle large volumes of image, video, and text data with high accuracy and flexible tooling options.

DIGI-TEXX provides outsourced data annotation services designed to support AI and Machine Learning model training. The company focuses on labeling and annotating structured and unstructured data, helping businesses turn raw datasets into reliable training data.

With a team of specialized data annotators and an experienced crowdsourcing workforce, DIGI-TEXX can manage projects of any size or complexity while maintaining consistent quality. Its workflows combine human expertise with AI-assisted tools to improve efficiency and accuracy.

DIGI-TEXX supports image, video, and text annotation, including multilingual text labeling and transcription. Projects can be delivered on the client’s existing systems or on DIGI-TEXX’s annotation platforms, making it suitable for companies with specific technical or security requirements.

Standout Features: Flexible annotation workflows, AI-assisted labeling tools, multilingual text annotation, and the ability to scale annotation teams quickly.

Pros:

• Handles high-volume data annotation projects efficiently.
• Supports multiple data types: image, video, and text.
• Flexible tooling options (client system or DIGI-TEXX platform).

Cons: May not be ideal for teams looking only for self-service annotation software.

2. 1840 & Company

Best for: U.S. companies that need outsourced data annotation services and want a reliable partner to build, manage, and scale global annotation teams with full workforce support.

1840 & Company provides outsourced data annotation services through a people-first model. With access to vetted professionals in over 150 countries, the company supports image, video, audio, and text annotation for a wide range of AI and machine learning projects.

Instead of offering only an annotation platform, 1840 & Company handles the entire workforce process, including hiring, training, payroll, compliance, and team scaling. This allows businesses to focus on AI development while the annotation operations are managed end to end.

The company works across multiple industries and supports various AI use cases, from NLP to 3D point cloud labeling. Its proprietary AI-powered Talent Cloud helps quickly vet and match businesses with suitable annotation professionals, usually within two weeks, making it suitable for both short-term and long-term projects.

Standout Features: A proprietary AI-powered Talent Cloud for fast vetting and accurate talent matching. Strong expertise across all major data types, including image annotation services, video data annotation, audio data labeling, and document processing.

Pros:

• Access to vetted annotation professionals in more than 150 countries.
• Helps reduce costs by up to 70% while maintaining labeling accuracy and quality.
• Manages payroll, compliance, and HR in addition to annotation work.

Cons:

• Not a niche, tool-only annotation provider, as it focuses more on workforce flexibility.
• May not be suitable for companies looking for a pre-built annotation tool only, such as Labelbox.

3. Scale AI

Best for: Businesses with highly specialized image and video annotation projects where accuracy and precision are more important than large-scale volume.

KeyMakr is a specialized provider and one of the Data annotation companies in USA, focused on delivering high-precision labeling for computer vision projects. Unlike large, generalist vendors, the company prioritizes customization and quality, making it a strong choice for businesses looking for outsourced data annotation services tailored to complex visual data requirements. KeyMakr supports tasks such as semantic segmentation, bounding boxes, object detection, and video annotation using a human-in-the-loop approach to ensure consistent accuracy.

KeyMakr is commonly chosen by companies that require high-quality training data for AI and machine learning models in industries such as retail, smart home technology, and security, where attention to detail directly impacts model performance.

Standout Features: Strong customization capabilities for complex data types, including 3D point cloud labeling and pixel-level image segmentation.

Pros:

• Known for meticulous and accurate annotation services.
• Ideal for small to mid-scale projects with detail-heavy requirements.
• Flexible annotation workflows that can be customized to client needs.

Cons:

• Limited scalability compared to enterprise-level providers like Scale AI or iMerit
• Primarily focused on computer vision, making it less suitable for NLP- or audio-heavy projects.

4. Surge AI

Best for: AI labs and NLP-focused startups that need reliable support for text-heavy projects and natural language processing tasks.

Surge AI is a popular partner for teams building large language models and conversational AI systems. Unlike general vendors, Surge AI focuses mainly on text-based work, making it a strong option for companies looking for outsourced data annotation services focused on text annotation, keyword tagging, and reinforcement learning from human feedback (RLHF).

These services help improve how chatbots, virtual assistants, and generative AI systems understand and respond to user input. Surge AI’s annotators are trained to handle language nuance, making the company especially effective for sentiment analysis and entity recognition tasks.

Standout Features: Strong specialization in text annotation and NLP labeling, including advanced RLHF for AI and machine learning models.

Pros:

• Deep expertise in NLP and conversational AI.
• A strong partner for companies developing generative AI and large language models.
• Able to scale text annotation and review teams quickly.

Cons:

• Primarily focused on language-based tasks, so less suitable for large-scale image or video annotation projects.
• Reliance on contractor networks may raise concerns about long-term workforce consistency.

5. Sama

Best for: Companies looking for reliable data annotation outsourcing while working with an ethical and socially responsible provider.

Sama (formerly Samasource) is a well-known company in the data annotation space, recognized for combining technical expertise with responsible workforce practices. As a Certified B Corp, Sama focuses on creating stable job opportunities in underserved communities, especially in Africa, while delivering high-quality annotation services to global clients.

Sama provides outsourced data annotation services across multiple data types, including image, video, audio, and text annotation. The company is particularly strong in computer vision projects such as object detection, object tracking, and 3D point cloud labeling, supporting AI teams that require accurate and reliable training data.

Sama works with fast-growing industries like autonomous vehicles and robotics, where data quality and consistency are critical. The company places strong emphasis on ethical labor standards, proper training, and long-term workforce development, making it a suitable partner for enterprises that value both performance and responsible sourcing.

Standout Features: Strong expertise in video annotation services, image annotation services, and AI data annotation services for high-growth industries such as autonomous vehicles and robotics.

Pros:

• Well-known for ethical outsourcing and responsible workforce practices.
• Able to scale data annotation projects across image, video, audio, and text data.
• A good fit for enterprises that value both data quality and social responsibility.

Cons:

• Has faced past scrutiny related to working conditions in certain content moderation and annotation projects.
• Pricing may be higher than some low-cost providers due to investments in training and ethical standards.

6. iMerit

Best for: Enterprises operating in highly regulated or accuracy-driven industries such as healthcare, autonomous driving, and financial services.

iMerit is a well-established provider of outsourced data annotation services, known for working with complex and regulated datasets. The company offers trained teams capable of handling image, video, text, and audio annotation, making it a trusted partner for projects where precision and compliance are critical.

iMerit is commonly used in sectors like medical imaging, autonomous vehicles, and financial services – industries that demand strict quality control, regulatory compliance, and highly accurate labeling to ensure safety and reliability.

Standout Features: Advanced annotation capabilities for computer vision models, including 3D point cloud labeling and semantic segmentation, making iMerit a reliable choice for large-scale and high-complexity AI projects.

Pros:

• Strong track record working with Fortune 500 companies and leading tech firms.
• Specializes in high-risk annotation projects where accuracy directly affects safety or compliance.
• Scalable annotation solutions across multiple data types, supported by global delivery centers.

Cons:

• Higher pricing compared to some providers, reflecting its specialization and quality standards.
• May feel too complex or resource-heavy for early-stage startups with smaller annotation needs.

7. Playment (TELUS Digital)

Best for: Enterprises that need high-volume image and video annotation projects, especially in autonomous driving and retail.

Playment, now part of TELUS Digital, is a well-known provider of outsourced data annotation services and is recognized among the top data annotation companies for its strong focus on computer vision. The company built its reputation by delivering image annotation, video annotation, and 3D point cloud labeling for industries such as autonomous vehicles, robotics, and retail analytics.

After joining TELUS Digital, Playment gained access to a global workforce and enterprise-level infrastructure. This allows the company to handle large-scale annotation projects and deliver high-quality training data faster, making it a good fit for enterprises with tight timelines.

Playment is especially strong in visual data annotation tasks, helping AI teams improve model performance while maintaining consistent quality at scale.

Standout Features: Deep expertise in object detection, object tracking, semantic segmentation, and bounding box annotation. Integration with TELUS Digital provides global delivery capabilities, along with enterprise-grade security and compliance.

Pros:

• Strong expertise in computer vision and 3D annotation.
• Backed by TELUS Digital’s global scale and infrastructure.
• Good balance between accuracy and scalability for large AI projects.

Cons:

• May feel less flexible for smaller clients due to corporate processes.
• Mainly focused on vision-based annotation, with limited support for text or audio data.

8. KeyMakr

Best for: Businesses with highly specialized image and video annotation projects where accuracy is more important than volume.

KeyMakr is a specialized provider of outsourced data annotation services, focused on delivering highly accurate labeling for computer vision projects. Unlike large, general-purpose vendors, KeyMakr emphasizes customization and data quality. The company handles tasks such as semantic segmentation, bounding boxes, object detection, and video annotation using a human-in-the-loop approach to ensure precision.

KeyMakr is often chosen by companies that need high-quality training data for AI and machine learning models, particularly in industries like retail, smart home technology, and security – where small details can have a major impact on model performance.

Standout Features: Strong customization capabilities for complex data types, including 3D point cloud labeling and pixel-level image segmentation.

Pros:

• Known for careful, accurate labeling and annotation services.
• A strong fit for small to mid-sized projects with detailed requirements.
• Flexible annotation workflows that can be tailored to specific client needs.

Cons:

• Limited scalability compared to enterprise-level providers such as Scale AI or iMerit.
• Primarily focused on computer vision, making it less suitable for large NLP or audio annotation projects.

9. Turing

Best for: Companies that need domain experts for complex or sensitive data annotation projects, rather than large-scale, generic annotation work.

Turing is widely known as a global talent marketplace for remote engineers. In response to growing demand for AI training data, the company has expanded into offering outsourced data annotation services through a talent-driven model.

Instead of operating like a traditional annotation provider, Turing focuses on connecting businesses with vetted annotation professionals who can handle complex and specialized tasks. This approach gives companies direct access to skilled human talent, rather than relying only on fully managed annotation services.

Standout Features: A talent-first model that emphasizes domain expertise and accurate skill matching, making Turing suitable for annotation tasks beyond basic image annotation.

Pros:

• Access to vetted experts for specialized AI data annotation projects.
• Flexible engagement model that allows teams to scale up or down quickly.
• Well-suited for tasks that require human judgment and domain-specific knowledge.

Cons:

• Not a fully turnkey annotation service provider like Sama or iMerit.
• Best suited for companies willing to manage annotation workflows more directly rather than fully outsourcing them.

10. Label Your Data

Best for: Companies in regulated industries that need high-quality training data with strict security and compliance requirements.

Label Your Data is a boutique provider of outsourced data annotation services, focused on delivering secure, scalable, and customizable annotation solutions. The company is trusted to handle sensitive raw data, including healthcare images, financial records, and government information, thanks to its strong compliance and information security practices. Label Your Data is recognized as one of the best data annotation companies to work for, making it a reliable choice for businesses seeking secure and precise annotation services.

Label Your Data supports image, video, text, and audio annotation, making it suitable for a wide range of industries. What sets the company apart is its commitment to data protection throughout the entire annotation pipeline, from data intake to final delivery.

In addition, Label Your Data places strong emphasis on regulatory compliance, including GDPR, and offers tailored workflows to meet specific AI data annotation requirements.

Standout Features: Strong focus on security and compliance, including GDPR adherence, along with customized workflows for AI data annotation services.

Pros:

• Strong compliance standards, including ISO/IEC 27001, PCI DSS, and GDPR.
• Flexible engagement models for different annotation projects.
• Delivers secure annotated datasets across image, video, text, and audio.

Cons:

• Smaller scale compared to large annotation providers like Appen or iMerit.
• Best suited for projects where security and precision matter more than high-volume cost optimization.

How to Choose the Right Data Annotation Partner

The data annotation services market is booming, but not all providers are truly good. Choosing the wrong partner can lead to poor quality data, wasted months of AI development effort, and security risks. That’s why businesses should carefully evaluate top data annotation companies based on the following criteria before making a decision:

Quality and QA Process

This should be your top priority. Don’t just believe the 99% accuracy claim. Ask specifically: How does your QA process work? Do you use a single-layer or multi-layer testing model? What metrics do you measure quality with (e.g., IoU, F1-score)? A reputable provider will be willing to conduct a free or low-cost pilot project so you can directly evaluate the quality of the output.

Security & Compliance

Ask for proof of security certifications. Is it ISO 27001 certified? How is it GDPR compliant? Ask about specific security measures: Will company data be encrypted? Are employees allowed to bring personal devices into the work area? How do you handle PII data?

Scalability and Human Resources

Ask about the actual size of their team. How many full-time annotators do they have? How many data points did they handle the largest project? How long would it take them to double the size of their team for a project if needed? A good partner should be able to flexibly scale to your business needs.

Technology & Tools

Some vendors use in-house software, while others use commercial tools. Important: Do their tools support the complex types of annotations you need? Are they flexible to work on their own platform if required? The best data annotation companies often use ‘AI-assisted annotation’ to increase speed and reduce costs.

Domain Expertise

Labeling medical data is very different from labeling self-driving cars. Ask them if they have experience in the same field as you. Having domain expertise helps them understand the context and significantly reduces errors, especially when dealing with new and difficult cases.

Communication & Support

When choosing an offshore annotation company, effective communication is very important. Is there a dedicated Project Manager? How often are they reporting and how transparent are the quality metrics?

Why choose outsourced data annotation services at DIGI-TEXX?

When comparing Top data annotation companies, businesses are not just looking for a vendor, but a reliable long-term partner who can deliver consistent quality, protect sensitive data, and scale as AI projects grow. DIGI-TEXX meets these expectations through proven experience, strong quality control, and a flexible delivery model tailored to real-world AI development needs.

• Proven expertise in large-scale data annotation projects: DIGI-TEXX has extensive experience handling millions of data points across image, video, text, and audio annotation for AI and Machine Learning.
• Strict multi-layer QA/QC process: Every dataset is reviewed through multiple quality control stages to ensure high accuracy and consistent results.
• Cost-effective offshore delivery model: Our offshore annotation teams help businesses significantly reduce operational costs without compromising data quality.
• Advanced annotation tools and AI-assisted workflows: We use modern annotation platforms combined with AI-assisted labeling to improve speed, accuracy, and scalability.
• Strong data security and compliance: DIGI-TEXX follows strict security protocols and complies with international data protection standards to safeguard sensitive and confidential data.
• Flexible scalability to support growing AI projects: Our large, well-trained workforce allows us to quickly scale resources up or down based on project requirements.

Frequently asked questions about outsourced data annotation services

Is data annotation all coding reddit?

The answer is NO. Data annotation is not all about coding. Data annotation is mainly focused on labeling images, text, audio, and video so AI systems can learn from accurate, structured data.

Can AI be used in data annotation technology?

The answer is Yes. AI tools can support data annotation by automating tasks like bounding boxes, semantic segmentation, and text labeling. However, human review is still essential to ensure accuracy, contextual understanding, and high-quality training data for AI models.

>>> Read more: Data Quality Assurance: What It Is and Best Practices

It can be seen that in the race to develop Artificial Intelligence, the quality and volume of training data is the deciding factor for success or failure. Trying to build everything in-house is quickly becoming outdated, expensive, and ineffective. Cooperating with a reputable provider of outsourced data annotation services like DIGI-TEXX not only helps you save significant costs and time but also ensures the highest quality of input data. If you are ready to upgrade your AI project with accurately annotated data, contact DIGI-TEXX for a free consultation and start a pilot project today.

>>> Read more: Data Quality Assurance: What It Is and Best Practices

DIGI-TEXX Contact Information:

🌐 Website: https://digi-texx.com/

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✉️ Email: [email protected]

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