Unlock AI without giving up your data
Train, evaluate, and deploy models directly on your own infrastructure—preserving privacy, meeting compliance, and enabling secure collaboration.
Enterprises can't centralize their most sensitive data without running into risk and red tape. Innovation slows, and regulated industries fall behind on AI adoption.
- Compliance hurdles slow or block projects
- Limited GPU access in the cloud creates bottlenecks
- Sovereignty rules restrict cross-border collaboration
webAI brings AI to the data, ensuring privacy and sovereignty from the start.
- Federated training and evaluation across distributed datasets
- Privacy by design with zero raw data movement
- Sovereign MLOps on customer-owned hardware
Core Capabilities
Train AI across multiple sites or organizations without exposing raw data.
Zero data transfer with encryption and auditability at every stage.
Build, deploy, and monitor AI entirely within your own infrastructure.
Enable collaboration that respects jurisdictional and compliance requirements.
Why It Matters: AI shouldn't require sacrificing privacy or sovereignty. By eliminating data movement and operating on customer hardware, enterprises gain the benefits of advanced AI while reducing compliance risk, cutting cloud costs, and unlocking collaboration that was previously blocked.
Use Cases
Train models on patient data across hospitals without sharing sensitive records.
Detect fraud patterns across institutions while keeping customer data private.
Build AI on classified technical manuals and operational data without moving it offsite.
The webAI Difference
Every step of the pipeline is designed for regulated environments.
Complete auditability and compliance with strict sovereignty rules.
Demonstrated success in defense, healthcare, and finance—making sovereign AI not just possible, but economical.