AI today is centralized. A few companies decide what is ethical, who gets access, and who profits. This approach is fragile, goes against democratic values, and is hostile to pluralism.
Pearl Protocol changes this. It is a decentralized federated learning platform where communities build and own AI models together without surrendering their data or values. Participants train locally with full control of their privacy and receive personalized models that adapt to their world. They also contribute to shared intelligence and earn from the value they create.
AI is becoming the most important infrastructure of our time. But today's AI is built for scale, not for diversity.
One model. One language. One set of values. One worldview.
This doesn't just exclude communities—it actively erases them. Indigenous languages disappear from training data. Local knowledge is ignored. Cultural nuances are flattened. Privacy is sacrificed for convenience.
The result? AI that serves a few, not the many.
Pearl Protocol exists because we believe in a different future:
→ Where communities train AI on their own terms
→ Where your data stays yours, always
→ Where models adapt to your world, not the other way around
→ Where contributors own what they build
→ Where governance is democratic, not dictatorial
This isn't just better AI. It's necessary AI.
Train models without sharing raw data. Your data stays local while contributing to global intelligence.
Communities train AIs that reflect their needs, values, and language.
No central server. No gatekeeper. Just a permissionless protocol anyone can join.
Train models on your device with your data, while improving a shared global model.
Contributors co-own the models they help train. Value flows back to those who create it.
Powerful CLI tools and APIs. Built by developers, for developers.
Five simple steps from idea to deployed model
Create a model and set the relevant model parameters
Find participants or models to collectively train
Everyone trains locally on their own data, privately
Combine model updates securely into a personal and a shared global model
Allow others to deploy your model and share rewards with contributors
Built on open, decentralized infrastructure
On-chain governance and rewards
Decentralized model distribution
Mathematical privacy guarantees
Pearl Protocol powers privacy-preserving AI across industries.
Indigenous communities train AI that understands their language, culture, and values—models that reflect their worldview, not Silicon Valley's
Each patient gets a model fine-tuned to their unique health data, while contributing to medical research without exposing private information
Regional journalism organizations collaborate on content recommendation models that respect local perspectives and editorial values
Schools create personalized learning models that adapt to each student's pace and style, while sharing insights across diverse educational communities