Top Data Annotation And Labeling Companies In 2027

Meta Description: Compare 11 data annotation companies for 2027: managed labeling teams and expert networks for image, video, text, audio, RLHF and LLM evaluation work.

Every machine learning model is shaped by the people who label its training data. Bounding boxes on street scenes teach a car to see pedestrians, transcribed audio teaches a voice assistant to understand accents, and ranked responses teach a large language model which answers people prefer. As models have moved from research to production, the work of preparing, labeling and checking that data has grown into a large outsourcing market of its own, served by data annotation companies (often called data labeling companies) that supply trained people, quality processes and often the software too.

This list is for machine learning engineers, data science leads, AI product managers and founders who need human annotation at scale. It covers image, video, LiDAR, text and audio labeling, as well as the newer work of reinforcement learning from human feedback (RLHF), supervised fine-tuning data, red-teaming and model evaluation. It includes managed-service firms that provide dedicated labeling teams and platform companies that pair their software with a vetted workforce. If you are new to outsourcing in general, our pillar guide to business process outsourcing explains the wider market these firms sit in.

Going into 2027, the market looks quite different from a few years ago. Demand has shifted from simple image tagging toward expert work: doctors reviewing medical outputs, lawyers and coders writing and grading model answers, and native speakers checking multilingual output. Several providers have been bought or have raised large rounds, so ownership and focus can change quickly. And buyers now ask more pointed questions about how annotators are paid and treated, alongside the usual questions on accuracy and security.

Each company below was checked on its own website to confirm that it currently sells annotation or labeling services (not only software), where it is based and what security credentials it lists. The list mixes managed-service providers with platform and workforce companies so you can compare both models. Companies whose sites showed no current annotation offering were left out.

Data Annotation Companies At A Glance

Company	Headquarters	Best For	Core Services
iMerit	San Jose, California, USA	Medical, autonomous mobility and generative AI projects needing a managed expert workforce	Image, video, LiDAR and text annotation, RLHF, model evaluation, Ango Hub platform
Sama	USA	Computer vision and GenAI teams wanting high first-batch acceptance and an impact workforce	Image, video, 3D point cloud and text annotation, model evaluation
CloudFactory	Founded in Nepal	Teams wanting a dedicated, trained labeling team that stays on the project	Image, video, text and audio labeling, human-in-the-loop review
Appen	Sydney, Australia	Multilingual data collection and annotation at very large scale	Data collection, annotation, RLHF, SFT, red-teaming in 235+ languages
Scale AI	San Francisco, California, USA	Frontier model builders and public sector AI programs	Data engine for annotation, RLHF, red-teaming, evaluation
Labelbox	San Francisco, California, USA	AI labs needing expert human data alongside labeling software	Labeling platform, Alignerr expert network, RL environments, evaluations
Toloka	Amsterdam, Netherlands	LLM teams needing domain experts for evaluation and fine-tuning data	Expert annotation, human evaluation, red-teaming, quality control
Innodata	Ridgefield Park, New Jersey, USA	Enterprises wanting a public company with in-house experts	Image, video, LiDAR, text, audio annotation, SFT data, RLHF and DPO
Shaip	New York, USA (Ubiquity)	Healthcare and conversational AI projects with compliance needs	Data collection, annotation, medical data labeling, de-identification
Cogito Tech	Levittown, New York, USA	Mid-sized computer vision and NLP projects across many verticals	Image, video, 3D, text and audio annotation, RLHF, red-teaming
TaskUs	New Braunfels, Texas, USA	AI safety, red-teaming and RLHF programs alongside trust and safety	RLHF data, AI safety and alignment, model testing and QA

Best Data Annotation Companies In 2027

1. iMerit — 10,000+ Active Resources And 25,000+ Domain Experts Across 60+ Countries

iMerit was founded in 2012 by Radha Basu and is headquartered in San Jose, California, with regional offices in New Orleans, Kolkata and Bengaluru (see our list of BPO companies in India for the wider Indian delivery market). It is a managed-service provider at heart: clients get trained, dedicated annotation teams rather than an anonymous crowd. The company reports a pool of more than 10,000 active resources across more than 60 countries, plus a Scholars program of more than 25,000 domain experts for specialized work such as medical and generative AI evaluation.

Its focus areas are generative AI, autonomous mobility and ADAS, healthcare AI, robotics and agricultural AI. iMerit also runs Ango Hub, its own annotation and model evaluation platform, so clients can use iMerit’s tooling or have teams work in their own. The company states it is SOC 2, ISO 27001, GDPR, HIPAA and TISAX compliant. TISAX is the automotive industry’s security standard, which is relevant for self-driving and driver-assistance projects.

iMerit is now part of EXL following an acquisition, so buyers should ask how that affects pricing, account management and roadmap. It suits companies with complex, high-stakes data such as radiology images, surgical video or sensor data for vehicles, where expert annotators and consistent teams matter more than the lowest unit price. Ask about the expertise of the specific team you would get and how medical or automotive guidelines are maintained over time.

2. Sama — 99% First-Batch Acceptance From A Certified B Corp Since 2008

Sama has helped AI teams label data since 2008. It offers image, video, 3D point cloud and text annotation, model evaluation, and Sama GenAI for foundation model work, including instruction following, preference ranking and factuality checks. Its verticals include autonomous vehicles and ADAS, banking and insurance, consumer and media, food and agriculture, retail and e-commerce, and robotics and manufacturing.

Quality is the headline claim. Sama reports a 99% first-batch acceptance rate, meaning most delivered batches pass client review without rework, and case studies cite similar accuracy figures. That metric is worth discussing in detail, because rework cycles are often the hidden cost in annotation projects.

Sama is a Certified B Corporation and was recently recertified. It was founded as an impact-sourcing organization and says it has built careers for more than 15,000 associates. It suits computer vision teams in automotive, retail and agriculture, and GenAI teams that want a managed partner with a published approach to workforce welfare. Ask where your team would be based, which security certifications cover those sites, and how Sama’s quality guarantees are written into the contract.

3. CloudFactory — Managed Labeling Teams With Dedicated Project Managers Since 2010

CloudFactory was founded in Nepal in 2010 and provides managed data labeling and human-in-the-loop services. Its model places vetted, trained workers in small teams with team leads, and multi-team projects get a dedicated project manager. The company stresses keeping the same labelers on a client’s project over time, so they build knowledge of the business rules and edge cases.

Its labeling services cover computer vision (bounding boxes, polygons, keypoints, cuboids, segmentation and redaction), text classification, audio transcription, generative AI image work and geospatial imagery. Quality is measured with gold-standard checks, sample review, consensus labeling and intersection over union for object detection. CloudFactory’s site displays ISO 9001, ISO 27001 and SOC 2 badges, and it describes background checks, NDAs, device restrictions and badged facility access for higher-security projects.

CloudFactory has also built out enterprise offerings in autonomous vehicles and robotics, transportation and logistics, oil and gas, and insurance, including damage assessment. It suits teams that want a stable, managed workforce rather than a fluctuating crowd, especially for long-running projects where guidelines evolve. Ask how team continuity is guaranteed, how fast teams can scale up for a new dataset and which delivery locations would handle your data.

4. Appen — 1 Million+ Contributors In 170+ Countries And 235+ Languages

Appen was founded in Sydney in 1996 and is listed on the Australian Securities Exchange (ASX: APX), with a US headquarters in Kirkland, Washington. It is one of the oldest firms in AI training data, and its scale is the main reason to consider it: more than one million vetted contributors in more than 170 countries, supporting more than 235 languages.

Appen sells custom data collection, annotation and evaluation, and its Frontier Alignment line covers RLHF, supervised fine-tuning (SFT) and red-teaming for large language models. It holds SOC 2 and ISO 27001 credentials. The breadth of languages and locales makes it a frequent choice for speech recognition, search relevance and multilingual LLM evaluation, where native speakers in many markets are required.

Appen suits large technology companies and model builders that need high volumes of data across many languages and regions. Because it relies heavily on a distributed contributor model, buyers with sensitive data should ask which work can be done in secure facilities and how contributor identity and quality are verified. Ask too how expert contributors are screened for specialized domains such as coding, math or medicine.

5. Scale AI — Data Engine And GenAI Evaluation For Frontier Model Builders Since 2016

Scale AI was founded in 2016 and is based in San Francisco. Its Data Engine helps clients collect, curate and annotate data across text, image, video and 3D sensor fusion including LiDAR, and its generative AI work covers RLHF (applying human preferences to model outputs), prompt-response creation, red-teaming and model evaluation. Scale says 90% of major generative AI model builders use its services.

Scale also runs public leaderboards and private benchmarks for model evaluation, and it serves government and national security customers through dedicated public sector teams and its Donovan product. It says 25% of its data contributors hold advanced degrees and that it has paid more than $1 billion to contributors globally.

Scale suits frontier AI labs, large enterprises and government programs that need high-volume, expert data for training and evaluation. Smaller teams may find its minimums and focus less suited to modest projects. Ask how domain experts are recruited and vetted for your project, what quality and turnaround commitments are offered, and how your data is separated from other clients’ work.

6. Labelbox — 2.6M+ Skill-Tested Experts In The Alignerr Network

Labelbox was founded in 2018 in San Francisco and began as labeling software for machine learning engineers. It has since added a large human data business. Its Alignerr expert network lists more than 2.6 million skill-tested experts across more than 200 domains who write rubrics, preferences and graded assessments for frontier models. Labelbox says more than 90% of the top US AI labs post-train and evaluate on its platform.

Labelbox now also builds reinforcement learning environments and evaluations through its Horizon product, and captures robotics demonstration data. It lists SOC 2 Type II, GDPR, CCPA, NIST 800-171 and PCI DSS among its compliance credentials, and it recently announced a Series E round of more than $100 million.

Labelbox suits teams that want both strong labeling software and access to expert human data from the same company, especially AI labs doing post-training and evaluation. Teams that only need software can use the platform with their own workforce. Ask how experts are tested and matched to your domain, how quality is scored and whether you can see individual expert performance.

7. Toloka — Expert Contributors In 90+ Domains, 70% With Advanced Degrees

Toloka was founded in 2014 by Olga Megorskaya and is headquartered in Amsterdam, with offices in the US, Israel, Switzerland and Serbia. It sells professional annotation, quality filtering, expert human evaluation and red-teaming, mainly for teams building and testing large language models and AI agents. In 2025 Jeff Bezos led a new investment in the company.

Toloka reports more than 6,000 active contributors who are skilled experts in more than 90 domains, with over 70% holding advanced degrees. Its crowd spans more than 100 countries and 40 languages. It describes more than 50 automated quality control methods and more than 60 anti-fraud methods built into its platform, and it holds ISO 27001 and ISO 27701 certifications with stated SOC 2, GDPR, CCPA and HIPAA compliance.

Toloka suits LLM developers that need specialist evaluation and fine-tuning data in areas such as coding, math, law, medicine and science, and teams that want a platform-led approach with strong automated quality checks. Ask how many experts are available in your specific domain, how their work is reviewed, and whether you can run a short pilot to compare their output with your internal benchmarks.

8. Innodata — 11,500+ In-House Experts Across 20+ Delivery Locations

Innodata is headquartered in Ridgefield Park, New Jersey, and is listed on Nasdaq (INOD). It has more than 36 years of history in data engineering and content services and now focuses heavily on AI data. Its annotation services cover image, video, LiDAR and sensor data, text, documents, audio and speech, and it sells supervised fine-tuning datasets and human preference optimization through RLHF and DPO.

The company says it has more than 11,500 in-house subject matter experts across more than 20 global delivery locations. Working with in-house staff rather than an open crowd can make security, consistency and confidentiality easier to manage. Innodata also offers federal solutions that it describes as certified, compliant and procurement-ready.

Innodata suits enterprises and model builders that want a publicly listed vendor with long experience in data processing and a large in-house workforce. Its document and content background makes it a natural fit for legal, financial and publishing data. Ask which delivery locations would handle your project, what certifications apply there and how expert reviewers are assigned for specialized topics.

9. Shaip — SOC 2 Type II And HIPAA Compliant Annotation From 12 Delivery Centers

Shaip has been operational since 2019 and provides AI training data collection, annotation and labeling. Its services include image segmentation and object detection, 2D and 3D annotation with sensor fusion, and medical data annotation, along with data sourced and curated from more than 60 countries. In February 2026 it joined Ubiquity Global Services, which is headquartered in New York, giving it access to 12 delivery centers across four continents.

Shaip lists GDPR, HIPAA, ISO 9001:2015, SOC 2 Type II and ISO 27001 credentials. Its workforce combines taskers, domain experts, native speakers and dedicated QA teams running multi-level audits. Its verticals include healthcare AI, conversational AI, computer vision, physical AI, e-commerce and generative AI.

Shaip suits healthcare AI teams that need annotated clinical data with strong compliance, and conversational AI projects that need speech and text data in many languages. Because the Ubiquity combination is recent, ask how account management works, which delivery centers would handle your data and how medical annotators are credentialed.

10. Cogito Tech — Annotation Across 12 Verticals With ISO 27001, SOC 2 And HIPAA Credentials

Cogito Tech is based in Levittown, New York, with roots dating back to 2011. It provides managed annotation teams for computer vision (image, video and 3D point cloud), NLP (text, audio and video transcription, sentiment and intent classification, named entity recognition) and generative AI work including RLHF, red-teaming and fine-tuning data. It also offers content moderation and document processing.

The company lists GDPR, ISO 9001, SOC 2, CCPA, HIPAA and ISO 27001 credentials. It serves a wide range of verticals, including agritech, autonomous vehicles, e-commerce, financial services, geospatial, insurance, logistics, medical, proptech, retail, robotics and security and surveillance.

Cogito Tech suits mid-sized AI teams and startups that want a managed, platform-agnostic workforce for computer vision and NLP projects without the minimums of the largest providers. Its breadth of verticals means it has likely seen projects similar to yours. Ask where its annotation teams are based, how many annotators would work on your project and what quality metrics it commits to.

11. TaskUs — RLHF, Red-Teaming And AI Safety From A Trust And Safety Specialist

TaskUs is best known for content moderation and trust and safety, and it has extended that experience into AI services. Its AI offering covers data feedback and evaluation (RLHF and synthetic data augmentation with human-in-the-loop processes), AI safety and alignment (red-team design and policy-driven data) and model testing and quality assurance using gold-standard benchmarks.

The company lists a long set of security credentials on its site, including PCI DSS Level 1, SSAE 16 Type II, HITRUST and HIPAA, and it has been rated a Leader in the Everest Group Trust and Safety Services PEAK Matrix for three consecutive years. That background matters for safety work, where annotators review harmful content and need wellness support.

TaskUs suits AI companies that need red-teaming, safety evaluation and RLHF at scale, particularly where the work overlaps with policy enforcement. It is less focused on traditional computer vision labeling than iMerit, Sama or CloudFactory. Ask which modalities its AI teams handle, where the work is done and how annotator wellbeing is supported on sensitive projects.

How To Choose A Data Annotation Company

Do You Need A Managed Service, A Platform Or Both?

Some teams already have labeling software and only need a trained annotation workforce. Others have people but need tooling, or need both. Managed-service firms supply a dedicated, trained workforce and project management, usually working in your tool or theirs. Platform companies sell software and often attach a vetted workforce. Decide which part you want to own: the tool, the workforce, the quality process or all three. If your data is sensitive or your guidelines change weekly, a dedicated managed team is often easier to steer than an open crowd.

How Will Quality Be Measured And Enforced?

Ask how the provider measures accuracy, for example through gold-standard tasks, consensus scoring, review layers or inter-annotator agreement. Ask what accuracy it commits to in the contract, how disagreements are resolved and how quickly guideline changes reach every annotator. Request a paid pilot on a sample of your own data and score the output yourself before you scale.

Does The Workforce Have The Right Expertise?

Simple image boxes can be drawn by a general workforce. Medical imaging, legal text, code evaluation and safety red-teaming cannot. For LLM work such as preference ranking, supervised fine-tuning data and model evaluation, ask how annotators are screened for subject expertise and writing ability, and what share of the team holds relevant degrees or professional experience. Language coverage matters too if your model serves several markets.

How Is Your Data Protected?

Training data often includes personal data, proprietary documents or unreleased product images. Ask for current security certifications such as ISO 27001 or SOC 2 Type II, and whether work happens in secured facilities, on managed devices or on personal machines. For health data, ask about HIPAA controls. Confirm that the provider will not reuse your data and that annotators sign confidentiality agreements.

How Are Annotators Treated And Paid?

Workforce practices affect quality and reputational risk. Ask whether annotators are employees or crowd contractors, how they are paid, and what support exists for those reviewing disturbing content. Several firms publish impact or fair-pay commitments, and some hold third-party certifications. If your project involves harmful content review, compare their approach with providers on our list of content moderation companies, which face the same welfare questions.

Conclusion

The data annotation market now splits into two broad groups. Managed-service firms such as iMerit, Sama, CloudFactory, Innodata, Shaip and Cogito Tech provide dedicated, trained teams and project management. Platform and network companies such as Scale AI, Labelbox, Toloka and Appen combine software with large pools of contributors and experts. TaskUs brings a trust and safety background to RLHF and red-teaming. Many buyers use more than one provider, for example a managed team for core computer vision work and an expert network for LLM evaluation.

Start with a paid pilot on your own data, score the output against your own standards and compare total cost, including rework and your team’s review time. If your AI program also needs adjacent services, see our lists of data entry outsourcing companies and back office outsourcing companies, and our comparison of BPO and KPO, which explains why expert annotation increasingly looks like knowledge process outsourcing.