Human data
for frontier AI
Built for complex AI challenges at scale.
Trusted by the teams leading AI innovation.
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Data products for modern AI development
One partner across training, alignment, and evaluation, from code and text to image, video, speech, and sensor data.
Frontier model alignment
Expert-generated data for the highest-stakes stage of training: chain-of-thought reasoning traces, subject-matter-expert RLHF, supervised fine-tuning demonstrations, adversarial red teaming, and knowledge rubric design. Built with credentialed specialists - clinicians, attorneys, quantitative researchers - rather than generalist annotators.
Agentic AI training data
Reinforcement learning environments, golden trajectories, tool-use traces, and automated verifiers for agents that plan and act across multi-step workflows. Rubrics are written per task so a partially correct trajectory is scored as partially correct, not simply failed.
Multimodal AI datasets
Aligned text, image, video, audio, and document data for vision-language and any-to-any models. Cross-modal grounding is annotated explicitly - region-to-phrase, timestamp-to-utterance - so alignment is supervised rather than inferred.
Physical AI training data
Egocentric video, teleoperation demonstrations, and multi-sensor capture for robotics, autonomy, and embodied systems. Includes LiDAR-camera fusion, 3D cuboid and semantic segmentation, and purpose-built collection in real operating environments.
Model evaluation and integrity
Independent, third-party assessment of model behavior: safety and bias testing, human preference benchmarking, hallucination and factuality scoring, and red-team probing. Evaluation is run by teams separate from those who produced your training data.
AI research services
Dedicated research pods for work that does not fit a standard annotation spec: novel task design, pilot studies, annotation guideline development, and custom methodology built alongside your research team. Embedded linguists, data scientists, and project leads.
Rigor built into every stage
Accuracy at scale comes from who does the work, and how the quality process is engineered.
Methodology Refined Over Decades
Data pipelines and quality processes proven across successive generations of AI model architectures.
Calibrated, Auditable Quality
Contributor calibration against gold-standard examples, inter-annotator agreement measurement, multi-round independent review, and statistical sampling of every dataset.
Global Reach, Real Dialects
A vetted contributor network captures the code-switching, accents, and cultural context generic datasets systematically underrepresent.
Configurable to Your Program
Datasets, modalities, taxonomies, and QA workflows configured around how your program actually runs.
Who does the work, and how you can check it
Every program runs with a named delivery lead and specialist annotators matched to your modality and domain, so you can see who is making quality decisions and trace how they were made.
Named delivery lead
One point of contact accountable for scope, timeline, and quality across your program.
Specialist teams by domain
Annotators selected for direct experience with your data type and subject matter.
Pilot before you scale
Every engagement starts with a scoped dataset to prove out quality and workflow fit.
Delivery on your terms
Datasets handed off in the format and integration method your pipeline already uses.