> For the complete documentation index, see [llms.txt](https://perle.gitbook.io/perle-docs/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://perle.gitbook.io/perle-docs/platform-overview/meet-perle-labs.md). # Meet Perle Labs **Perle Labs** is a **web3-native data quality layer** where verified experts contribute domain-specific knowledge to train, evaluate, and improve AI systems. Launched by the team behind [Perle.ai](https://www.perle.ai/) — led by veterans from **Scale AI**, **Meta**, **Amazon**, and **MIT** — Perle Labs channels verified human intelligence into enterprise AI data pipelines. Our platform combines expert validation, onchain transparency, and reward incentives to ensure that every labeled dataset, model evaluation, and feedback cycle is accurate, auditable, and fairly rewarded. ### What Is Perle Labs? Traditional data-labeling models rely on anonymous crowdsourcing, which leads to inconsistent quality, unclear accountability, and misaligned incentives. Perle Labs replaces this with a system where expertise is verified, every contribution is tracked onchain, and every reward is earned transparently. Contributors complete structured tasks, from annotation to evaluation, and earn rewards based on **accuracy, consistency, and complexity**. These points represent both tangible rewards and onchain reputation. Each contributor’s journey involves: * **Training Modules:** Domain-specific onboarding and qualification tasks. * **Annotation & Evaluation Tasks:** Structured data labeling and validation challenges. * **Reputation Tracking:** Accuracy and reliability measured over time. * **Badge System:** Recognition for verified performance and milestones. * **Provenance Records:** Immutable onchain logs of every action. Together, these components align real human expertise with decentralized incentives, creating a transparent, high-trust data ecosystem where quality and accountability scale globally. ### Why Web3? Perle Labs runs on the Solana blockchain, chosen for its speed, scalability, and negligible transaction costs. This infrastructure enables: * **Transparent provenance:** Every contribution is immutably logged onchain. * **Instant payments:** Rewards are distributed algorithmically in real time. * **On-chain reputation:** Contributor performance is verifiable and portable across ecosystems. * **Compliance-ready records:** Enterprise partners can audit data pipelines end-to-end. * **Global inclusion:** Anyone, anywhere, can participate and be rewarded fairly. Solana’s speed and efficiency allow Perle Labs to scale expert knowledge globally without compromising quality, precision, or trust. ### Who We Are **Perle Labs** is led by a multidisciplinary team combining expertise in **AI data operations, blockchain infrastructure, and product design**. Our mission is to make human expertise a verifiable digital asset, measurable, auditable, and rewarded. This initiative extends [Perle.ai](https://www.perle.ai/)’s broader vision: to connect enterprise-grade AI development with decentralized human contribution, ensuring that the people who help train AI are recognized and compensated transparently. ### **Funding and Growth** In **mid-2025**, Perle Labs raised **$9 million in new seed funding**, led by **Framework Ventures**, bringing total funding to **$17.5 million**. The announcement saw rapid traction, with nearly **30,000 new followers** and over **300,000 social engagements** within a week, signaling strong interest from both web3 and AI communities. ### **Our Vision** Perle Labs is redefining how verified human knowledge powers intelligent systems. By aligning transparent incentives with measurable expertise, we’re building a future where doctors, linguists, engineers, and researchers can directly train, evaluate, and govern AI — and be fairly rewarded for the intelligence they help create. --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter: ``` GET https://perle.gitbook.io/perle-docs/platform-overview/meet-perle-labs.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.