Trustworthy AI can’t exist without trustworthy data. And right now, the data behind AI is becoming harder to verify. Models are training on synthetic content, scraped datasets, and feedback loops that are difficult to trace. Human review still happens, but it is often anonymous, fragmented, and disconnected from any lasting record of who contributed, what they verified, or how reliable their work was. But by verifying contributors, tracking performance, and recording each validation step, AI data can become accountable. Experts can build reputation over time. High-quality work can be routed to higher-value tasks. Enterprises can see the human judgment behind the datasets their systems rely on. It’s why we’re building Perle Labs: expert-validated, human-verified, on-chain auditable data infrastructure for AI systems that need to be trusted in the real world.