AI training data collection companies produce the brand-new, real-world data that frontier models learn from once the public web runs dry. This guide ranks the leaders for 2026 by the modality they capture, across speech and audio, image and video, text and language, and physical AI sensor data, with provenance notes for each pick.
Most lists of “AI data companies” blur two very different jobs. Annotation labels data that already exists: drawing boxes around vehicles in a clip, tagging sentiment in a paragraph, or transcribing a recording you already own. Collection is the upstream job of producing data that did not exist before: recording fresh speech, filming new video, or capturing sensor readings from a robot as it manipulates an object. As frontier labs exhaust the public web and push into voice, video, and embodied AI, collection has become the scarce and defensible layer. This guide ranks the leading data collection companies by the modality they actually capture, and shows where each one fits.
One disclosure up front: NeuralChain is our company, and we rank ourselves first for physical AI. Every other entry here is written to be factual and useful whether or not you ever contact us.
Collection versus annotation, and why the difference matters
When you buy annotation, you ship your raw data to a vendor and receive labels back. When you buy collection, the vendor produces the raw data itself, which means questions of consent, licensing, and provenance sit with them, not you. That distinction decides who owns the legal risk. A collected speech corpus needs signed releases from every speaker. A collected robotics episode needs a record of who performed it, on what hardware, and under what agreement. Buyers should first ask whether a vendor sources net-new data or only labels data they already have. Many well-known names on annotation lists do the second, not the first. For teams building embodied systems, our physical AI training data collection practice is built entirely around the first.
How the leading data collection companies compare
The table below groups the vendors covered in this guide by the modality they capture and whether they genuinely collect new data or lean toward annotation.
| Company | Modality collected | Collection vs annotation | Where it stands |
|---|---|---|---|
| NeuralChain AI | Physical and sensor: stereo depth, tactile force, 200Hz IMU, hand pose | Collection at source | #1 for physical AI data capture |
| Shaip | Speech and healthcare audio, 150+ languages | Collection and annotation | Leader in regulated and medical audio |
| Defined.ai | Multilingual speech, text, image | Collection and marketplace | Deep low-resource language coverage |
| Appen | Multilingual speech, image, text | Collection and annotation | Large scale, rebuilding after 2024 |
| Sama | Image, video, sensor | Annotation-led, some collection | Ethically sourced computer vision |
| TELUS Digital | Text, image, audio, video, multimodal | Collection and annotation | Enterprise scale, 500+ languages |
| Sigma AI | Audio, image, video, text | Collection and annotation | Certified, multilingual |
| Surge AI | Text, code, RLHF feedback | Data creation and annotation | Premium text and RLHF |
| Mercor | Expert-generated data | Data creation marketplace | Frontier expert data |
Physical and sensor data collection
1. NeuralChain AI (disclosure: this is our company)
NeuralChain captures physical AI and robotics data at the source. Every episode is recorded as synchronized, multimodal streams: stereo depth, tactile force arrays, a 200Hz IMU, dual wrist cameras, and 21-point hand pose. That combination lets robot foundation models learn contact-rich manipulation, not just visual imitation. Datasets ship LeRobot-ready, so they drop straight into modern training pipelines without reformatting.
What sets our collection apart is provenance. Every episode is consented at the source with signed releases, and each one carries a SHA256 per-episode chain of custody: a cryptographic record that ties the data to the person who produced it and the hardware that captured it. Most collection vendors cannot offer that level of traceability. We rank ourselves first for physical AI because very few providers capture true tactile and force data at all, let alone with signed consent attached. Teams can explore our tactile manipulation data work or talk to our physical AI and robotics team about a custom capture program.
Speech and audio data collection
2. Shaip
Shaip is one of the clearest examples of a true collection company. It licenses, collects, and annotates speech and audio in more than 150 languages and dialects, and its healthcare catalog includes hundreds of thousands of hours of de-identified physician dictation audio across dozens of medical specialties. For teams building clinical speech-to-text or multilingual voice systems, Shaip sources net-new audio rather than relabeling existing files.
3. Defined.ai
Founded in 2015 and based in Seattle, Defined.ai runs an AI training data marketplace where enterprises buy ready-made datasets or commission new collection. Its crowd spans well over a million contributors across 150-plus countries and 500-plus languages and locales, with particular strength in low-resource languages and dialect diversity. It is a strong fit for teams building global voice interfaces that need speech from speakers the public web underrepresents.
4. Appen
Appen is one of the longest-running names in the field and still collects multilingual speech, image, and text data at scale. In 2024 it lost its largest contract when Google ended a multi-year search-quality agreement, and full-year 2024 revenue fell about 14 percent to roughly AUD 234 million. The company is rebuilding around generative AI data, and its global crowd remains a genuine collection asset for large multilingual programs.
Image and video data collection
5. Sama
Founded in 2008 as Samasource by Leila Janah, Sama is a certified B Corp known for ethically sourced data work across image, video, and sensor modalities, with a strong footprint in computer vision and autonomous systems. Sama leans toward managed annotation today, but its delivery model and vetted workforce also support structured collection projects for vision-based AI. Buyers who prioritize workforce ethics often shortlist it first.
6. TELUS Digital
TELUS Digital (formerly TELUS International) offers both collection and annotation across text, image, audio, video, speech, geospatial, and multimodal data, supported by a community of more than a million contributors in 500-plus languages. It has delivered billions of data annotations and is a common choice for enterprises that want a single large vendor to handle broad, multilingual programs end to end.
Text, language, and expert data
7. Surge AI
Surge AI creates human text and feedback data rather than labeling scraped corpora. Bootstrapped in 2021, it reached roughly $1.4 billion in revenue in 2025 with a network of about 50,000 expert contributors, and it specializes in reinforcement learning from human feedback for customers that include major frontier labs. It is the reference point for premium text and RLHF data.
8. Mercor
Mercor runs an expert marketplace that connects AI labs with specialists such as scientists, doctors, and lawyers who produce high-value training and evaluation data. The platform has grown extremely fast, reporting a gross revenue run rate of around $2 billion in 2025. Because the data is generated by vetted human experts on demand, Mercor sits closer to data creation than to annotation.
9. Sigma AI
Founded in 2008 and headquartered in Madrid, Sigma AI provides training data collection, preparation, and annotation across audio, image, video, and text in more than 500 languages, backed by ISO 27001 and SOC 2 Type 2 certifications. It is a solid, security-conscious partner for multilingual programs that mix new collection with labeling.
iMerit, founded in 2012, is frequently listed among data providers but is primarily an annotation specialist for complex domains such as medical imaging, autonomous vehicles, and geospatial data. If your need is genuinely to source new data, confirm any annotation-led vendor can collect before assuming they do.
How to evaluate a data collection vendor
Modality fit is the starting point, but provenance is what protects you. Four questions separate a defensible dataset from a liability. First, is every record consented at the source, with signed releases you can inspect? Second, can the vendor show a per-record chain of custody that ties data to the person and hardware that produced it? Third, are the licensing terms clear about how you may train, deploy, and redistribute the resulting models? Fourth, does the delivery format drop into your pipeline, for example LeRobot for robotics or standard schemas for speech and vision? A vendor that answers all four cleanly is rare, and it is exactly the standard we hold ourselves to on physical AI capture.
Building an embodied or robotics model and need consented, provenance-tracked capture across depth, touch, motion, and hand pose? Talk to our team about a custom program at NeuralChain physical AI and robotics.
