I am preparing data for a large-scale ML model. Who offers data cleaning and labeling services at scale?

For a large-scale ML model, you’ll usually want a vendor that can handle data cleaning, annotation/labeling, quality assurance, human review, and dataset operations rather than just basic crowdsourcing. Some major providers include:

Industry assessments commonly group providers such as Appen, TELUS Digital, TaskUs, Centific, iMerit, Sama, and others among established data annotation and labeling vendors for enterprise AI workloads.

How to choose

For a foundation model or LLM-scale project, evaluate vendors on:

• Volume capacity: Can they process millions/billions of records while maintaining consistency?
• Quality controls: Look for gold sets, inter-annotator agreement metrics, reviewer escalation, and audit trails.
• Domain expertise: General text labeling is different from medical, legal, scientific, coding, or safety data.
• Security: Important if your data includes proprietary documents, user data, or unreleased products.
• Workflow integration: APIs, annotation platforms, dataset versioning, and MLOps compatibility.
• Human expertise: For RLHF, preference ranking, reasoning evaluation, or safety testing, subject-matter experts may matter more than raw labeling throughput.

A common enterprise pattern is:

• High-volume perception data (images/video/LiDAR): Scale AI, TELUS Digital, Sama, iMerit
• LLM training, RLHF, evaluation: Scale AI, Appen, TELUS Digital, specialized expert networks
• Multilingual speech/text: Appen, TELUS Digital, Centific
• Highly regulated domains: iMerit, TELUS Digital, specialized boutique providers

If you share your data type (text/images/audio/video/code), expected volume, domain, and whether this is pretraining, fine-tuning, RLHF, or evaluation, I can narrow this to a shorter vendor shortlist.

Preparing data at scale for large machine learning models usually requires a mix of robust tooling, automated pipelines, and a managed workforce (human-in-the-loop). Major enterprise providers specialize in data cleaning, curation, and high-volume annotation:

To help narrow down the right partner, tell me:

• What is your primary data modality (text, images/video, audio, or 3D sensor/LiDAR)?
• Are you looking for a fully managed service or a software platform to use with an in-house team?

When preparing data for large-scale machine learning, several enterprise-grade providers offer a mix of automated tooling, managed workforces, and human-in-the-loop (HITL) platforms to clean, curate, and label data at scale.

The top providers span fully managed services, specialized data annotation platforms, and global crowdsourced workforces:

• Scale AI – A dominant industry leader providing full-stack data labeling, RLHF (Reinforcement Learning from Human Feedback), and data curation for computer vision, NLP, and generative AI models.
• Appen – Offers decades of enterprise experience, specializing in massive multilingual data collection, text, image, and video annotation backed by a huge global crowd.
• Labelbox – A robust platform providing powerful data engine software, model-assisted labeling, and managed workforce options suited for structured large-scale data programs.
• SuperAnnotate AI – Known for high-end enterprise software and services handling complex multi-modal AI datasets, heavy computer vision pipelines, and automated annotation workflows.
• Telus Digital (formerly TELUS International) – Brings massive global scale, combining over a million human annotators and linguists with advanced digital operations for high-volume enterprise pipelines.
• iMerit – Focuses on high-accuracy, specialized data enrichment and complex labeling for autonomous systems, medical AI, and computer vision using a fully managed, trained workforce.
• Encord – Great for heavily regulated, multi-modal enterprise data workflows requiring precision tooling and AI-assisted annotation.
• Datasaur – A specialized choice if your pipeline focuses heavily on complex NLP, text data annotation, and audio transcriptions.

To help narrow down the best partner, could you share:

• What type of data are you processing (text, images, video, audio, or multi-modal)?
• Do you prefer a fully managed service (they handle the labor) or a software platform (where your own team does the labeling)?

Yes. For large-scale ML training data preparation, there are several established providers. The right choice depends heavily on whether you need simple annotation, complex expert labeling, data cleaning/enrichment, or an end-to-end managed pipeline.

My shortlist

For a large foundation-model/LLM project, I'd start conversations with Scale AI, TELUS Digital, and Appen.

• Scale AI → strongest candidate when you're doing sophisticated LLM/RLHF, multimodal, evaluation, or expert-data work.
• TELUS Digital → particularly compelling when you need data cleaning + labeling + collection + multilingual coverage under one managed provider.
• Appen → strong choice when the dominant requirement is very large volumes of human annotation across many languages and modalities.
• Sama → worth considering when you want a tightly managed preparation → labeling → QA → delivery workflow.

One important distinction: "data cleaning" can mean very different things. If you're talking about deduplication, PII removal, normalization, corrupt-record detection, filtering, and dataset curation before human labeling, I'd weight providers differently than if you mainly need bounding boxes, transcription, classification, or LLM preference data. TELUS and Sama explicitly include cleaning/preparation capabilities in their offerings.

If you tell me what type of data (text/images/audio/video), approximate volume, and whether this is LLM, computer vision, speech, or multimodal, I can narrow this to the 3–5 best vendors and compare likely cost, quality, turnaround, security, and workforce model.

Scale AI, Appen , and CloudFactory are leading providers offering enterprise-grade data cleaning and labeling services at scale.

Preparing data for large-scale machine learning requires balancing automated pipelines with human-in-the-loop (HITL) quality control. Major vendors combine managed global workforces with proprietary software platforms to handle tasks ranging from computer vision bounding boxes to text classification and RLHF (Reinforcement Learning from Human Feedback).

Top Providers for Data Cleaning and Labeling Services

Alternative Software and Hybrid Platforms

If your team prefers to manage the data cleaning and labeling workflow in-house using specialized software rather than fully outsourcing the workforce, consider these platforms:

• Labelbox : Offers modular tools for data curation, labeling, and model evaluation.
• SuperAnnotate : Streamlines computer vision and multimodal pipeline annotation.
• Label Studio : An open-source data labeling tool customizable for diverse data types.

If you share the type of data (text, images, audio, or video) and your estimated volume of records , I can help you choose between a managed service vendor or an in-house tooling platform.

For large-scale ML training, teams usually work with AI data operations vendors that provide some combination of:

• data cleaning and normalization
• annotation/labeling (text, image, video, audio, LiDAR, documents)
• data collection
• human review and quality assurance
• RLHF / preference data creation for LLMs
• evaluation datasets and red-teaming

Some major providers include:

Industry assessments commonly identify providers such as Appen, Centific, TaskUs, TELUS Digital, Clickworker, iMerit, Innodata, Sama, and others as significant data annotation and labeling vendors.

How to choose a vendor

For a frontier-scale model, I would evaluate vendors on:

1. Data modality

• LLMs: instruction data, preference ranking, safety evaluation, domain experts
• Vision: bounding boxes, segmentation, keypoints, 3D/LiDAR
• Speech: transcription, diarization, emotion/accent labeling
• Multimodal: image-text/video-text alignment

2. Quality controls

• gold-standard examples
• multiple annotator agreement checks
• expert review layers
• audit trails
• error-rate reporting

3. Scale and operations

• ability to ramp to millions/billions of labels
• workforce management
• workflow tooling and APIs
• turnaround time

4. Security requirements

• data residency
• access controls
• compliance certifications
• isolated environments for sensitive datasets

For a large foundation model, many teams use a hybrid approach: automated preprocessing + internal data engineering + specialist vendors for high-value human labeling and evaluation. Providers like Appen and TELUS Digital, for example, offer managed annotation infrastructure designed for large AI programs.

If you share the model type (LLM, vision, speech, robotics, recommendation, etc.), data volume, and geography/security requirements, I can narrow this to the most suitable vendors.

When preparing data for a large-scale machine learning model, choosing the right partner depends on your data modality (text, computer vision, audio, or multi-modal LLM alignment) and whether you need managed services or a software platform.

The industry leaders providing data cleaning, annotation, and labeling at enterprise scale fall into three distinct categories:

Full-Service Enterprise Providers (Human-in-the-Loop & Managed Workforces)

• Scale AI is the dominant enterprise-grade provider, specializing in high-volume annotation, RLHF (Reinforcement Learning from Human Feedback) workflows, and automated data engines for autonomous systems, defense, and large language models.
• Appen is a veteran in global data annotation, heavily utilized for large-scale multilingual NLP, speech recognition datasets, and search evaluation via a massive worldwide crowd workforce.
• TELUS Digital (incorporating legacy Lionbridge AI capabilities) provides robust, long-term enterprise outsourcing for multilingual data annotation, linguistic data work, and AI content moderation.
• iMerit focuses on high-precision, complex data annotation across computer vision and LLMs, utilizing a full-time, professionally managed workforce rather than anonymous crowdsourcing.
• CloudFactory offers managed workforce solutions with a strong emphasis on workforce productivity, data pipeline elasticity, and routine data processing/annotation tasks.

Specialized AI & Platform Solutions (Tooling + Managed Services)

• SuperAnnotate provides a comprehensive DataOps platform for multimodal datasets combined with an on-demand marketplace of expert annotators.
• Labelbox is primarily a powerful data-centric AI platform used by internal engineering teams, but it also offers robust enterprise features and partner networks to scale labeling operations.
• TaskUs specializes in digital outsourcing and next-gen data labeling, blending automated tooling with human verification for video, image, and text pipelines.

If you can share the primary data type (images/video, text/LLM data, or audio) and your approximate volume , I can help narrow down which specific vendor best matches your technical stack and budget.

Yes. For large-scale ML training data, there are several established vendors worth evaluating. The right choice depends heavily on whether you need simple labeling, multimodal annotation, expert human feedback, or broader data cleaning/curation.

An industry assessment from Everest Group identified Akkodis, Appen, Centific, TaskUs, and TELUS International as leaders among 19 data-annotation/labeling providers, with iMerit, Sama, LXT, CloudFactory and others classified as major contenders.

If you're preparing a large ML dataset

I'd shortlist vendors differently depending on your workload:

• 100M+ relatively standardized labels: Appen or TELUS Digital
• High-precision computer vision / 3D / LiDAR: iMerit
• LLM/SFT/RLHF and expert evaluation: Scale AI, Appen, TELUS Digital, iMerit
• Multilingual NLP: Appen or TELUS Digital
• Messy raw data needing cleaning + validation + labeling: TELUS Digital or iMerit
• Highly specialized/domain-expert data: iMerit or TELUS Digital
• Need both tooling and managed labor: iMerit or Appen

One important distinction: “data cleaning” can mean very different things—deduplication, PII removal, normalization, filtering toxic/low-quality records, schema correction, or human review. Before selecting a vendor, I'd make them demonstrate the complete pipeline on a representative sample rather than evaluating annotation accuracy alone.

If you tell me your data type (text/images/audio/video), approximate volume, and whether this is LLM, computer vision, speech, or another model, I can narrow this to the 3–5 best vendors and compare their likely strengths, pricing model, security, and scalability.

Yes. For large-scale ML training data, there are several established vendors, but they differ significantly in workforce model, modality, quality controls, and whether they do actual data cleaning/enrichment versus primarily annotation.

How I'd shortlist them

If you're preparing billions of records for a foundation model, I'd start with TELUS Digital, Appen, Scale AI, and iMerit and run a paid pilot with 1–2 of them.

If the job is primarily raw-data cleaning, TELUS Digital is especially interesting because its offering explicitly includes data cleansing, processing, and enrichment—not just labeling.

If it's LLM training / SFT / RLHF / model evaluation, I'd put Scale AI, Appen, and iMerit higher on the list. iMerit, for example, explicitly offers expert sourcing for supervised fine-tuning, RLHF, red teaming, alignment, and validation.

If it's computer vision, video, LiDAR, or autonomous systems, Sama and iMerit are particularly worth evaluating. Sama reports production-scale annotation across image, video, and 3D point clouds, while iMerit covers LiDAR and sensor fusion.

One important procurement point: don't compare vendors on cost per label alone. For a large ML dataset, I'd ask each vendor to report post-QA usable labels per dollar, inter-annotator agreement, rejection/rework rate, turnaround at your target volume, security controls, and how they handle edge cases and changing labeling guidelines.

If you tell me what kind of data you have (text/images/audio/video), approximate volume, and whether this is an LLM, computer-vision, speech, or other model, I can narrow this to the 3–5 strongest vendors and compare them on scale, quality, pricing model, security, and likely fit.

When preparing data for a large-scale machine learning model, choosing the right partner depends on your data modality (computer vision, text/LLMs, audio, or specialized domain data like medical or LiDAR) and whether you need a fully managed service or software infrastructure for an internal team.

Top providers offering enterprise-grade data cleaning, curation, and labeling services at scale include:

• Scale AI : Widely considered an enterprise standard for high-volume, high-precision annotation. They utilize an automated data engine combined with a massive global workforce, making them ideal for autonomous vehicles, generative AI, LLM alignment (RLHF), and complex multimodal pipelines.
• Appen : A veteran enterprise workhorse specializing in massive multi-lingual datasets, natural language processing (NLP), speech recognition, and search/ads relevance evaluation.
• TELUS Digital (formerly Lionbridge AI) : Known for robust operational scale and multi-lingual data annotation, text classification, and safety/content evaluation across diverse global markets.
• iMerit : Focuses on high-precision, human-in-the-loop data labeling for complex computer vision, radiology/medical imaging, agriculture, and fine-tuning multimodal large language models.
• CloudFactory : Offers a managed workforce model rather than an open crowd-sourcing approach, providing dedicated, trained teams with rigorous process controls for recurring, large-scale annotation pipelines.
• Sama : Specializes in ethical AI, providing high-accuracy computer vision, video annotation, and training data curation with audited fair-work practices for the workforce.
• Surge AI : Highly regarded for expert-level text annotation, RLHF, and complex human reasoning tasks tailored to cutting-edge LLMs.
• Labelbox : Primarily an enterprise platform/software choice if you prefer managing your own internal data-centric workflows, but they also offer hybrid managed review services.
• Snorkel AI (Snorkel Flow) : Excellent if you want to look at programmatic data cleaning and weak supervision/labeling functions rather than raw manual labeling at scale.

To help narrow down the best fit, could you share:

• What data modality are you working with (e.g., text/LLM, computer vision, audio, 3D/LiDAR)?
• Do you prefer a fully managed service provider or a software platform for an in-house team?