Permissionless Token Generation, Zero Data Exposure

Business Continuity
How do I run my business when I can't access tokens?
Economics
Is my return on AI spend justifying the soaring costs?
Operational Freedom
Why can't I decide my own quotas, rate limits, and moderation policies?
Data & Compliance
What am I allowed to send to AI and who has access to it?
THE EMERGING BUSINESS RISK
If you cannot generate tokens, you cannot run the business
Once critical processes depend on AI, relying entirely on a third party for tokens is the same class of risk as power and internet. Continuity and IT risk plans need capacity you control — completely permissionless, and impossible for a vendor to quota or rate-limit.
OWNED CAPACITY, PROVABLE TRUST
Contracted capacity only solves the risk if token generation stays inside your perimeter. Shipping data to a vendor "private AI" or contractual clean room recreates the same continuity, moderation, and compliance exposure — just with better marketing. Legal reviews and audits are process controls, not physical or architectural barriers. You need cryptographic proof that data never leaves your domain, and that neither the vendor nor the operator can reach it.
PROXIML SOLUTION
Cryptographically Provable AI Clean Rooms
proxiML® Clean Rooms are specifically designed to run AI workloads, from traditional ML to computer vision to Agentic AI.
SECURE INSIGHTS AT SCALE
Turnkey Security with Fully Managed Clean Rooms
Programmatic Automation
Our Python SDK makes it easy to programmatically execute secure jobs on any infrastructure. Just provide the job specification and proxiML handles the rest.
Centralized Management
Manage and support 1000s of separate AI deployments through a single pane of glass. Reuse data integrations across AI workloads.
Accelerate Onboarding
Provision analytic services directly from any of our AI partners, instantly, without any lengthy contract negotiations, legal, or security reviews
Need help getting started?
Speak to one of our architects to get personalized on-boarding recommendations
