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Enhance your models with expert knowledge that meets the highest standards of accuracy, reliability, and ethical integrity.

Coding

Verified coders for agentic evaluation, code review, and coding-agent trajectory data. Teams ran 764 coder-targeted studies on Prolific in the last 12 months, including security-vulnerability evaluations for a national AI safety institute completed at 100% approval.

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Healthcare

Licensed doctors, nurses, and allied health professionals, checked against official registries such as the GMC and NPI, for clinical evaluation and patient-facing model review. 20,000+ verified healthcare professionals across 42 countries.

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Finance

Credentialed finance professionals for financial reasoning, compliance, and risk evaluation. We are actively growing this network and scope every study against your exact requirements.

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Expert knowledge, on demand

Access specialized knowledge that general data can't provide. Our rigorous verification process ensures you work with genuine experts who bring real-world experience to your AI development.

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Our in-house verification process ensures total quality control without delays. Meet regulatory requirements with confidence and build trustworthy AI systems.

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Find the expertise you need

Access a growing network of specialists, verified with our rigorous multi-stage process.

Improve your AI with expert insights

From specialized training to critical evaluation, domain experts enhance your AI at every stage of development.

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Domain-specific fine-tuning

Give your AI the specialized skills it needs. Train models with expert-labeled datasets to achieve human-level precision in fields like medicine, law, finance, and science.

Expert evaluations

Validate your AI’s outputs with real-world professionals. Ensure it delivers reliable, nuanced, and regulatory-compliant results under real-world conditions.

Safety and adversarial testing

Strengthen your models against failure points. Domain experts stress test models to uncover vulnerabilities, prevent misuse, and enhance model robustness.

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