I am preparing data for a large-scale ML model. Who offers data cleaning and labeling services at scale?
Data as of Sep 21, 2026 · Based on 289 AI responses from ChatGPT Search and Google AI Mode · See how Parse measures this
Data as of Sep 21, 2026 · Based on 289 AI responses from ChatGPT Search and Google AI Mode · See how Parse measures this
For large-
Brands AI recommends here
Named in 75% of answers
The industry leader for large-scale, enterprise-ready labeling, including RLHF and computer vision. It is the best fit for high-volume projects but may be less cost-effective for niche high-touch requirements.
Named in 67% of answers
Named in 55% of answers
Best for high-precision, complex domain-specific tasks in medical, geospatial, and robotic fields. It relies on trained experts rather than a broad crowd, ensuring higher consistency for specialized validation.
Named in 51% of answers
Source | Citation share |
|---|---|
 15 Best Data Labeling and Data Annotation Companies | Voxel51https://voxel51.com/blog/15-best-data-annotation-companies-data-labeling-services | 2% |
 Top 10 Human Data Providers in 2026: Full In-Depth Reviewhttps://www.herohunt.ai/blog/top-10-human-data-providers-full-in-depth-review | 2% |
The Ultimate AI Data Labeling Industry Overview (2026)https://www.herohunt.ai/blog/the-ultimate-ai-data-labeling-industry-overview | 2% |
 Scale AI - Wikipediahttps://en.wikipedia.org/wiki/Scale_AI | 1% |
 5 Best Data Annotation Companies in 2026 [AI Training Data Services & Pricing]https://www.lightly.ai/blog/best-data-annotation-companies | 1% |
 Data Services: Data Annotation, Data Labeling, and Data Validation » Aspiritechhttps://aspiritech.org/services/data-services/ | 1% |
Top 5 Data Annotation for AI Labs (Full Review 2026)https://www.herohunt.ai/blog/top-5-data-annotation-for-ai-labs-full-reviews | 1% |
 Top Data Labeling Companies in 2025https://www.reddit.com/r/u_RogermaxUSA/comments/1l3rx2i/top_data_labeling_companies_in_2025/ | 1% |
 Top 9 Leading Data Labeling Companies | 2026 Updatedhttps://www.gdsonline.tech/data-labeling-companies/ | 1% |
 7 Best Data Labeling Companies (2026)https://averroes.ai/blog/best-data-labeling-companies | 1% |
For large-scale ML training, teams typically use specialized **AI data services companies** that provide some combination of: - **Data cleaning** (deduplication, normalization, filtering, PII removal, quality checks) - **Data labeling/annotation** (text, image, video, audio, 3D, geospatial)
For large-scale ML training, teams typically use specialized AI data services companies that provide some combination of:
Some major providers include:
| Provider | Typical strengths |
|---|---|
| Scale AI | Large-scale annotation, RLHF, model evaluation, autonomous vehicles, LLM training data workflows |
| TELUS Digital | Global annotation workforce, multimodal labeling, data validation, multilingual projects; reports handling billions of labels annually and supporting text, image, audio, video, and geospatial datasets. TELUS Digital TELUS Digital |
| DataForce | Data collection, annotation, transcription, relevance rating, generative AI training, and large contributor networks for multimodal AI projects. DataForce DataForce |
| Appen | Human-labeled datasets, search relevance, NLP, speech, image/video annotation, and multilingual data projects. Appen USA |
| Sama | Computer vision annotation, image/video labeling, and managed data operations |
| iMerit | Specialized annotation for industries such as healthcare, geospatial, automotive, and enterprise AI |
| Innodata | Data preparation, document processing, NLP datasets, and enterprise AI workflows |
| CloudFactory | Managed human-in-the-loop data processing and annotation operations |
| Clickworker | Crowdsourced labeling, text/audio/image tasks, and evaluation workloads |
| LXT | Data collection, annotation, transcription, and multilingual AI datasets |
Industry assessments commonly group providers such as Appen, TELUS Digital, TaskUs, Centific, iMerit, Sama, LXT, and others among established data annotation and labeling vendors for AI/ML workloads.
For a foundation model or large-scale ML project, evaluate:
For a large foundation-model dataset, many organizations use a hybrid approach: automated preprocessing + internal tooling + external human labeling for ambiguous or high-value examples. The right vendor depends heavily on whether you are preparing text corpora for an LLM, multimodal data, autonomous systems data, medical data, or another domain.
When preparing massive datasets for large-scale machine learning models, several industry-leading providers combine managed workforces , data curation/cleaning pipelines, and enterprise annotation…
When preparing massive datasets for large-scale machine learning models, several industry-leading providers combine managed workforces , data curation/cleaning pipelines, and enterprise annotation platforms.
The top providers specializing in data cleaning, preprocessing, and labeling at scale include:
To help narrow down which provider fits your exact setup, tell me:
When preparing data for a large-scale machine learning or generative AI model, you typically choose between **fully managed service providers** (who supply the workforce and handle the pipeline end-to-end) or **enterprise software platforms** (which provide the tooling to manage your own internal or crowdsourced…
When preparing data for a large-scale machine learning or generative AI model, you typically choose between fully managed service providers (who supply the workforce and handle the pipeline end-to-end) or enterprise software platforms (which provide the tooling to manage your own internal or crowdsourced teams, often with embedded managed workforces).
The industry leaders providing data cleaning, curation, and labeling at scale are organized below by their primary approach:
These companies combine massive human workforces with advanced automated workflows to handle heavy data ingestion, filtering, cleaning, and annotation.
These platforms provide sophisticated software for data curation, error analysis, and visualization, while also offering direct access to vetted expert workforces.
To help narrow down the best partner, tell me:
Yes. For **large-scale ML training data**, there are several established vendors that handle cleaning/curation, annotation, validation, and sometimes data collection end-to-end. Provider | Particularly strong for | Scale / capabilities --- | --- | ---
Yes. For large-scale ML training data, there are several established vendors that handle cleaning/curation, annotation, validation, and sometimes data collection end-to-end.
| Provider | Particularly strong for | Scale / capabilities |
|---|---|---|
| scale.com | Multimodal, LLMs, computer vision, autonomous systems | Data collection, curation, labeling, evaluation; supports high-volume production workloads. Scale AI Scale AI |
| telusdigital.com | Multimodal enterprise datasets, multilingual work | Reports >1M AI experts and >2B labels/year; text, image, audio, video, 3D and geospatial data. TELUS Digital |
| appen.com | LLM training, NLP, speech, multimodal | Global workforce across 170 countries; annotation, collection, curation, fine-tuning and evaluation. Appen |
| dataforce.ai | Multilingual, speech/audio, NLP, image/video | 1M+ contributors, annotation and data collection; says it can onboard up to 1,000 annotators within weeks. DataForce |
| sama.com | High-quality human annotation and validation | Fully managed pilot-to-production annotation, validation and model evaluation. Sama |
| uber.com | Real-world/multimodal data, mapping, speech, computer vision | Collection and labeling infrastructure with a stated 10M+ contributor network and 25+ data types. Uber |
One important distinction: “data cleaning” can mean very different things—deduplication and normalization, PII removal, toxic-content filtering, annotation QA, dataset curation, or human review of model outputs. The right vendor depends heavily on which of those you need.
If you tell me your data type (text/image/audio/video), approximate volume, target languages, and whether this is LLM, CV, speech, or another model, I can narrow this to the 3–5 best vendors and compare likely pricing, workforce model, security, and strengths.
Yes. For large-scale ML training data, there are several established providers, but they differ significantly in whether they are primarily **managed human labeling**, **expert/LLM data work**, or **data-cleaning/curation platforms**. ### Strong options to evaluate -…
Yes. For large-scale ML training data, there are several established providers, but they differ significantly in whether they are primarily managed human labeling, expert/LLM data work, or data-cleaning/curation platforms.
| If your priority is… | Start with |
|---|---|
| Massive, diverse workforce | Appen / TELUS Digital |
| Managed end-to-end labeling | Sama / iMerit |
| High-quality specialist/domain data | iMerit |
| LLM/foundation-model training & evaluation | iMerit / Appen / Sama |
| Multimodal data + modern data infrastructure | Encord |
| Computer vision / LiDAR / sensor data | Sama / iMerit / TELUS |
| Multilingual data | Appen / TELUS |
One important distinction: "data cleaning" can mean very different things—deduplication, normalization, PII removal, filtering toxic/low-quality samples, fixing labels, resolving disagreements, or expert review. If you're preparing data for a foundation model/LLM, I'd put substantially more weight on vendors' capabilities for data curation, quality evaluation, expert annotation, preference data, and model-assisted labeling than on basic annotation throughput.
If you tell me your data type (text/image/audio/video), approximate volume, target model, and whether you need human experts, I can narrow this to 3–5 vendors and compare their likely fit, security/compliance, and pricing model.
Preparing data at scale for a large-scale machine learning or foundational AI model typically requires a mix of automated tools (model-assisted labeling) and managed human-in-the-loop (HITL) workforces.[](https://scale.com/guides/data-labeling-annotation-guide)…
Preparing data at scale for a large-scale machine learning or foundational AI model typically requires a mix of automated tools (model-assisted labeling) and managed human-in-the-loop (HITL) workforces.
The industry is divided into enterprise-grade platforms (which provide software and managed workforces), specialized/niche annotators , and raw crowdsourcing marketplaces.
These providers offer proprietary data engines, robust quality assurance (QA) pipelines, and dedicated or vetted annotator networks suited for heavy enterprise workloads.
If your ML model requires specialized expertise (e.g., medical imaging, legal text, complex geospatial/LiDAR data, or high-security clearance), consider these vendors:
If you already have your own pipeline/software tooling built out and just need raw volume or micro-task execution:
To help narrow down the best partner, tell me:
Preparing data for large-scale machine learning requires robust pipelines that merge automated data curation (cleaning, deduplication, outlier rejection) with human-in-the-loop (HITL) annotation. The industry leaders offering enterprise-grade data cleaning, curation, and labeling services at scale fall into three…
Preparing data for large-scale machine learning requires robust pipelines that merge automated data curation (cleaning, deduplication, outlier rejection) with human-in-the-loop (HITL) annotation.
The industry leaders offering enterprise-grade data cleaning, curation, and labeling services at scale fall into three distinct categories: fully managed service providers, enterprise software platforms with managed workforces, and hyperscale cloud providers.
These companies provide both the software infrastructure and the massive, managed human workforce to clean, filter, and label data.
If you prefer a software-first approach where you retain tight control over the ontology and workflows—or want to leverage model-assisted labeling—these platforms are standard:
If your data already lives in a specific cloud ecosystem and security/egress is a primary concern:
If you can share:
I can help you narrow down the best vendor for your specific architecture.
Yes. For **large-scale ML data cleaning, annotation, and labeling**, the main vendors I’d evaluate are: - **[scale.com](https://scale.com/data-engine?utm_source=chatgpt.com)** — Strong choice for large, complex production workloads. Its Data Engine covers data collection, curation, annotation, evaluation, RLHF, and…
Yes. For large-scale ML data cleaning, annotation, and labeling, the main vendors I’d evaluate are:
| If your priority is… | Start with |
|---|---|
| Frontier LLM / GenAI training | Scale AI, Appen |
| Massive multimodal labeling | Scale AI, TELUS Digital |
| Computer vision / LiDAR | Scale AI, iMerit, Sama |
| Multilingual data | TELUS Digital, Appen |
| Highly managed expert annotation | iMerit, Sama |
| Data curation + cleaning + labeling + evaluation | Scale AI |
| Very large global workforce | TELUS Digital, Appen |
One important distinction: “data cleaning” can mean very different things—deduplication, normalization, PII removal, taxonomy/ontology work, quality scoring, human annotation, or expert validation. For a large ML project, I'd generally avoid selecting a vendor based simply on “labels per hour”; QA methodology, worker expertise, data security, auditability, and the ability to handle edge cases tend to matter more.
If you tell me your data type (text/images/audio/video/3D), approximate volume, geography, and whether this is LLM or traditional ML, I can narrow this to the 3–5 best vendors and compare likely pricing, strengths, and tradeoffs.
For large-scale ML model development, teams typically work with specialized **AI data operations / data annotation vendors** that can handle data cleaning, enrichment, labeling, quality control, and human-in-the-loop workflows. Some of the major providers include: Provider | Best known for | Typical workloads --- |…
For large-scale ML model development, teams typically work with specialized AI data operations / data annotation vendors that can handle data cleaning, enrichment, labeling, quality control, and human-in-the-loop workflows. Some of the major providers include:
| Provider | Best known for | Typical workloads |
|---|---|---|
| Scale AI | Large-scale AI training data operations | LLM data preparation, RLHF, image/video annotation, autonomous vehicle data, model evaluation |
| Appen | Global human data collection and annotation | Text, speech, image, video, multilingual datasets, search relevance, LLM alignment Appen |
| TELUS Digital | Enterprise annotation at scale | Multimodal labeling, NLP, computer vision, audio, geospatial data, AI evaluation; it reports large global annotation operations and AI-assisted quality workflows. Telus Digital |
| Sama | Computer vision and enterprise AI data | Image/video labeling, model validation, specialized annotation workflows |
| iMerit | Domain-expert annotation | Medical AI, geospatial, autonomous systems, enterprise computer vision |
| CloudFactory | Managed workforce operations | Data labeling, document processing, human-in-the-loop AI workflows |
| TaskUs | Trust & safety and AI operations | Content moderation, annotation, AI data operations |
| Innodata | Data engineering + AI preparation | Document datasets, NLP, enterprise data transformation |
Industry assessments commonly identify providers such as Appen, TELUS Digital, TaskUs, Centific, Akkodis, iMerit, Sama, and others as major data annotation and labeling service providers.
For a large ML program, evaluate vendors on:
Data type support
Text: classification, extraction, RLHF preference ranking, instruction tuning
Images: bounding boxes, segmentation, keypoints
Video: tracking, temporal labeling
Audio: transcription, speaker labeling
Multimodal: image-text, video-language, sensor fusion
Quality controls
Multi-pass review
Inter-annotator agreement metrics
Gold datasets and calibration tasks
Expert review for edge cases
Security/compliance
Data residency requirements
Access controls
Private annotation environments
Compliance certifications
Scaling model
Dedicated expert teams vs. crowdsourcing
Ability to ramp from thousands to millions/billions of labels
Program management maturity
ML workflow integration
APIs
Dataset versioning
Annotation platforms
Export formats compatible with your training stack
If you are building a foundation model or large generative model, look for vendors with experience in:
For computer vision or robotics, prioritize vendors with strong 2D/3D annotation, LiDAR, video tracking, and sensor-fusion experience.
If you share your data modality (text/image/video/audio), approximate volume, domain (e.g., healthcare, autonomous driving, LLM), and whether you need human experts or low-cost labeling, I can narrow this to the most suitable vendors.
Preparing data at scale for large machine learning or foundation models requires a blend of automated tooling (AI-assisted pre-labeling, programmatic data cleaning) and scalable human-in-the-loop (HITL) workforces.[](https://scale.com/guides/data-labeling-annotation-guide)…
Preparing data at scale for large machine learning or foundation models requires a blend of automated tooling (AI-assisted pre-labeling, programmatic data cleaning) and scalable human-in-the-loop (HITL) workforces.
The industry leaders offering enterprise-grade data cleaning, curation, and labeling services at scale fall into a few clear categories depending on your workflow model:
To help narrow down which provider fits best, tell me:
