AI models are only as good as their training data. As AI systems grow more specialized, access to verifiable, expert-driven feedback that keeps pace with demand becomes a bottleneck.
For the past year, Perle has served AI teams and enterprise customers with a full-stack, expert-in-the-loop data annotation and workflow orchestration platform. Multi-modal annotation of images, videos, audio, text, and code is handled at scale, combining advanced tooling with diligent, expert-validated QA pipelines. But the demand for high-quality human feedback is growing faster than traditional infrastructure can support.
That’s why we created Perle Labs.
Scaling Expert AI Data Annotation
Built on Solana, Perle Labs is web3 contributor infrastructure purpose-built for scale, allowing us to open up participation to contributors worldwide. Annotators will use Perle Labs to review, label, and evaluate data, with every contribution logged onchain. As contributors complete structured training modules and tasks, they build verified reputations and earn rewards based on the quality of their work.
Solana provides the transaction throughput and near-instant finality that Perle Labs needs to process contributions quickly and affordably, enabling us to build a global network of verified experts with rigorous data quality. This onchain infrastructure supports:
1. Transparent attribution: Every annotation has verifiable provenance.
2. Fair compensation: Rewards are executed automatically and proportionally.
Verified Human Expertise for High-Stakes AI
Unlike general data marketplaces or AGI ventures, Perle Labs is laser-focused on delivering the critical human feedback that production-ready AI depends on. Even the best AI models can falter when faced with uncertainty and real-world edge cases.
In sectors like robotics, medicine, engineering, and law, data labeling defines whether a system performs safely and accurately. This means enabling qualified experts to contribute verifiable work, ensuring that they are properly compensated and attributed, and creating systems where quality and provenance are easily auditable to demonstrate data lineage for compliance.
- The platform onboards contributors with structured training and uplevels them based on their task completion and accuracy.
- Consistent, accurate performance unlocks access to more complex work and higher rewards.
- Contributors build reputations over time that are tied to specific subject-matter domains, and gain access to specialized tasks that match their expertise.
Perle Labs will function as a dynamic marketplace where AI projects get matched with qualified contributors who have the skills they need. The result is expert human feedback at scale.
Roadmap: Beta & Product Launch
Perle Labs will soon enter its beta phase and welcome an initial wave of early contributors. The upcoming beta release will demonstrate core task flows and reward mechanisms.
As the contributor network grows, coverage of edge cases and domain expertise will improve. Higher quality data will attract more enterprise projects, which will drive increased rewards and attract higher-quality contributors, generating a flywheel that benefits all participants.
Each milestone will focus on steady, purposeful growth to establish Perle Labs as a prime source of human-verified data that brings decentralized integrity, traceability, and trust to AI development.
Join the Perle Labs Community
Perle Labs is where verifiable human intelligence meets verifiable machine learning. Be sure to follow @PerleLabs and @solana on X, and join our Discord and Telegram to learn more about the beta and stay informed as we launch.
In the months ahead, our team will be expanding contributor access, refining task design, and forming early partnerships. Step by step, Perle Labs is building an open, scalable framework that puts human knowledge at the center of how advanced AI systems learn and operate.
