XASEAGI will not emerge from internet data alone.
The breakthroughs people talk about — curing cancer, improving healthcare systems, preventing financial crime, optimizing energy grids — require real world data.
Clinical records. Call center conversations. Financial logs. Operational systems.
That data already exists. But it lives inside regulated institutions.
Hospitals cannot export it. Banks cannot copy it. Enterprises cannot risk leaking it.
So AI is stuck training on what is public, not what is real.
If we want AI to move beyond chatbots and into civilization scale impact, it needs lawful access to real world data.
That is the bottleneck.
Xase enables AI Labs to access regulated data without transferring ownership, without breaking compliance, and without months of legal overhead.
Access is governed in runtime. Policies are enforced by code. Every use generates cryptographic evidence.
Real world data stays where it is. AI accesses it under rules.
If AGI is going to solve meaningful problems, it will need access to the systems that actually run the world.
We are building the infrastructure that makes that access possible.
import xase
client = xase.Client(api_key="sk_...")
# Request governed access
session = client.access(
dataset="medical-records-2024",
purpose="model-training",
duration="30d"
)
# Train without downloading
for batch in session.stream():
model.train(batch)
# Evidence generated automatically
print(session.evidence_url)Data holders set access rules: who, what purpose, how long, at what price.
AI labs authenticate and specify intent. Policy is evaluated at runtime.
Data never leaves. Every access logged with cryptographic evidence.
Talk to us about your use case.