The human feedback layer for physical AI

Robotics, embodied AI and physical AI models need real-world human data.
Commission the training data your model needs, even when it doesn't exist yet.

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Trusted by 100+ robotics and physical AI research teams

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The infrastructure physical AI teams run on

Our platform supports multimodal data capture natively so participants can upload and annotate images, video and audio directly within a structured task. Configure via CLI or API, no external tools required.

Commission the data your model needs

Collect images, video and audio directly from participants in a single workflow, with structured annotations attached at the point of submission.

Guided by structured prompts, reference materials and built-in quality checks. No platform switching or separate labelling rounds needed.

Filter participants with precision

200,000+ trusted participants across 35+ countries, filterable by age, ability, hand size, household context, device ownership and more. Experts across robotics, functional safety, mobility and medicine, verified by specialism.

Scale human eval for physical AI

Access via CLI or API. Scriptable in any pipeline, with webhooks on response and study completion so you can ingest incrementally.

Stable cohort hashes so the same participant group can be recalled for longitudinal comparison across model versions – critical for tracking safety perception as your robot evolves.

Why Prolific

Real-world human data for teams building physical AI

Embodied AI training

Training physical AI models requires real humans performing real tasks, in real environments. Commission egocentric video, manipulation task recordings, physical scene annotation and more. The data your model needs, built to your exact specification.

Physical model evaluation

Get human judgment at scale – rate robot task progress, compare policy rollouts and filter grasp descriptions against 3D scenes. Structured output that feeds directly into VLA, world-model and behaviour-cloning workflows, without leaving your pipeline.

Human-robot interaction

Validate robotics beyond lab conditions. Run acceptance, trust and safety perception studies with deployment demographics, before a public pilot. HRI surveys, structured interviews and hardware pre-tests to take your projects further.

In practice

2,000+ robotics projects run on Prolific

Physical-scene annotation · Grasp description & filtering · Robot video evaluation · Trajectory & motion preference · Acceptance & trust perception · Hardware UX pre-tests · HRI surveys & structured interviews · Specialist safety & red-team review

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How fast-moving AI teams use Prolific

Trusted by AI/ML developers, researchers, and leading organizations across industries.

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Unpacking human preference for LLMs - The HUMAINE framework

Our human-centered leaderboard ranks frontier AI models by how real, diverse users actually experience them — not just technical benchmarks. Featured at ICLR 2026.

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Building breakthrough AI faster

Ai2 reduced human data collection from weeks to hours with Prolific, building state-of-the-art multimodal AI models faster without sacrificing quality.

Gemini 3 Pro: Frontier safety framework

The frontier safety framework report for Google’s latest model.

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FAQ

Questions from teams collecting multimodal data

How quickly can we start collecting evaluation data?

Our platform is designed for immediate deployment. Self-serve video and preference projects launch in minutes, with results arriving within hours. Managed teleoperation or safety projects depend on scope, hardware integration, and evaluator specialisation requirements.

How much work will my team need to do versus what Prolific handles?

With our self-serve platform, you control the process. We provide infrastructure and participants. You design tasks - video review, preference, teleop, or survey - in your evaluation tool or our AI Task Builder, set criteria, and analyse results. With managed services, we handle everything from participant sourcing to quality assurance. You define requirements and get verified results.

How does Prolific ensure evaluation data quality for AI evaluations?

We combine participant verification, specialised qualification tests, credentials checks, performance tracking, and automated quality controls to maintain a high-quality pool. For physical AI evaluations, we recommend AI Taskers or Domain Experts when you need robotics, autonomy, mechanical, or safety expertise for your tasks.

Do you do LiDAR, point cloud, or 3D sensor annotation?

Prolific focuses on the human feedback layer: preference data, evaluation, acceptance research, and demographically controlled teleoperation. Most serious teams use us alongside an annotation vendor, not instead of one.

Can I run RLHF or preference data collection for a VLA or world model?

Yes. Side-by-side trajectory and video comparisons, Likert-scaled safety and naturalness ratings, and free-text rationale - all programmatic via API, with evaluator cohorts you specify and can reproduce across training runs.

Do you support teleoperation data collection?

Yes, through demographically specified participants using browser-based tasks or partner-integrated teleoperation tooling. The differentiator is who operates - age, body size, dexterity, language, culture - not the rig.

How does Prolific compare to traditional data vendors?

Traditional vendors use large annotation or teleop teams on hire, with little transparency into evaluator profiles and selection criteria. Prolific gives you direct access to verified evaluators through self-serve or managed options - the quality assurance of managed services, the transparency and control of direct access, and faster turnaround times.

Four roles that get the most from Prolific on physical AI

Foundation-model-for-robotics team needing demographically controlled demonstration data, plus practising roboticists for learned-policy and failure-mode review.
Humanoid or home-robot product team in or approaching pilot — real households, plus functional-safety engineers, to evaluate acceptance, naturalness, and edge cases.
Autonomous vehicle safety-case lead assembling human evidence for regulators, from end-user perception through practising-specialist review of edge cases.
HRI or embodied AI researcher running trust, perception, or interaction studies at a sample size beyond a single-site IRB.