Decentralized data layers
Coordinate high-quality datasets across networks without surrendering ownership, provenance, or control.
Protocol Partners gives developers open, verifiable infrastructure to connect data, models, and agents—without compromising privacy or control.
A modular stack designed to move intelligence safely across systems, organizations, and networks.
Coordinate high-quality datasets across networks without surrendering ownership, provenance, or control.
Connect models, agents, and data sources through a universal interface built for composable intelligence.
Train and deploy across untrusted environments with verifiable compute and privacy-preserving execution.
One secure path from private data to production-ready AI.
Data enters through encrypted, source-verified gateways.
Identity and provenance are proven at the protocol layer.
Models and agents coordinate across any supported network.
Outputs settle with portable cryptographic proof.
import protocol_partners as pp
agent = pp.Agent(name="atlas-scout")
@agent.on_task
def handle(task):
return agent.reason(task.prompt)
pp.connect(agent, network="mainnet")Import the protocol, describe your agent, and connect. No infrastructure to provision, no orchestration glue to maintain.
From install to a live agent in under five minutes.
One import and a decorator replace an entire service layer.
Drop it into any Python stack you already run.
One protocol layer that accelerates engineering teams and strengthens the bottom line.
Protection is not a layer bolted on top—it is enforced by the protocol itself at every stage.
Datasets stay under your ownership with provenance tracked at every hop of the protocol.
Every payload is encrypted in transit and at rest; keys never leave your environment.
Weights and inference run inside verified enclaves with tamper-evident execution proofs.
Our development timeline from core design through the public network launch.
Protocol specification, cryptographic primitives, and reference SDK architecture.
01 / 03Invited partners run agents on the private testnet with guided integration support.
02 / 03Public mainnet, open developer access, and permissionless agent deployment.
03 / 03Technical answers about integrating Protocol Partners into your stack.
Yes. The protocol operates at the data and orchestration layer, so it works alongside any training or inference framework. You keep your existing PyTorch, TensorFlow, or JAX pipelines — the SDK wraps datasets and model calls with encrypted, verifiable transport without changing your model code.
Integrate verifiable AI infrastructure in minutes. Use familiar tools, typed SDKs, and production-grade observability from day one.
import { ProtocolPartners } from "@protocolpartners/sdk";
const client = new ProtocolPartners({ network: "mainnet" });
const inference = await client.execute({
model: "foundation/atlas-3",
data: encryptedDataset,
policy: { verifiable: true, private: true }
});
console.log(inference.proof);Early access opens with the closed alpha. Tell us what you plan to build and we will reach out with an invite as capacity opens.
Secure deployments and custom infrastructure.
SDK access, grants, and technical support.
Dubai World Trade Centre Free Zone Establishment
Protocol Partners FZE is led by a global team focused on client success and operational excellence.
CEO / General Director
Song Jr. Frank Jun Yong