Mercor partners with enterprises to anonymize and contribute operational data to top AI labs. You get paid, and you see how AI will reshape your business.
Frontier AI labs can't build enterprise-grade models from public data alone. The next leap in AI capability requires operational data: how teams communicate, close deals, ship software, and run finance. That data lives inside your company.
Captured across your everyday tools: messages, meetings, documents, and project trackers.
Proprietary anonymization at every layer.
From first contact to signed agreement, our team handles everything. No engineering work required.
Get in touch →Know someone who would be a good fit?
If it works out, we'll pay you a referral bonus.
Send us the company and point of contact, and we'll handle the outreach directly.
Bonus paid 30 days after their first extraction.
Frontier AI models are increasingly being trained to work inside companies, navigating real tools, workflows, and decisions. Public data and synthetic datasets cannot replicate that. Your internal data captures how organizations actually operate, which is exactly what labs need to build AI that can handle real enterprise tasks.
Compensation is based on data volume, the tools you connect, and the depth of your data. Connecting more tools generally results in a higher payout.
No. Mercor covers all costs associated with data extraction, anonymization, and transfer. You receive compensation with no upfront costs, setup fees, or deductions from the payment.
The extraction and anonymization process is fully automated. No AI lab buyer or third-party vendor ever sees your raw data. Mercor's engineering team oversees the pipeline infrastructure but does not manually review your data. All access is logged and governed by strict internal controls.
Payment is issued via wire transfer upon delivery of the anonymized dataset to the buyer. This typically occurs within 2-4 weeks of the signed agreement, depending on data extraction and processing time.
Mercor's anonymization pipeline is designed to produce output that meets de-identification standards under GDPR and CCPA. Since the anonymized dataset contains no personal data, it falls outside the scope of most personal data regulations.