Keep your data private
while using the best AI models.
Use any LLM. Expose nothing. Proxy AI One is a control layer between your organization and the world's best AI models. Sensitive data is tokenized before it leaves, and restored after.
Built for safe AI deployment in enterprise environments
The problem
Your teams want to use AI. Your legal team says no.
Every prompt is a potential leak
When your employees use ChatGPT, Gemini or Claude, sensitive data like customer records, financial data or internal communications leaves your organization. Most teams don't realize it until it's too late.
"We can't allow that" is killing productivity
Legal and security teams block AI tools by default. Not because they don't see the value, but because the risk is real and the alternatives didn't exist. Until now.
Building your own LLM isn't the answer
Private models are expensive, slow to deploy, and always behind the frontier. Your teams need at least GPT-4, Gemini or Claude, and you can provide them with a safe way to use them.
How it works
Four steps. No sensitive data leaves. Ever.
User sends a prompt
An employee asks an AI tool to summarise a contract, draft an email or analyze a report. Just like they normally would. Nothing changes on their end.
Sensitive data never leaves
Before the prompt reaches any external model, Proxy AI One identifies and replaces sensitive information with anonymous tokens. Customer names, financials, internal IDs. Safe before they go anywhere.
Safe prompt reaches the LLM
The sanitized prompt is forwarded to your chosen model: OpenAI GPT-4 / ChatGPT, Anthropic Claude, Google Gemini, Meta Llama, Deepseek, Grok AI, Mistral AI, GitHub Copilot, Perplexity AI or any other. The model has no access to real sensitive data.
Your team gets a clean answer
Proxy AI One restores the original context before delivering the response. Your employee sees a complete, coherent answer. With their data intact and protected.
Original prompt
"Summarize the contract with Acme Corp for Jan 12, 2024. Point of contact: john.doe@acme.com."
Tokenized prompt (sent to LLM)
"Summarize the contract with [ORG_001] for [DATE_001]. Point of contact: [EMAIL_001]."
Why Proxy AI One
Everything you need to say yes to AI.
One control layer for all your AI traffic
Instead of managing risk per tool, per team or per model, Proxy AI One centralizes policies, logging and data protection across every LLM interaction in your organization.
Your data is never used to train external models
Tokenization ensures that what reaches the LLM is never your real data. No sensitive information is retained, shared or used to improve external models without your explicit consent.
Audit-ready from day one
Full visibility into what was processed, when and how. Proxy AI One is built to support GDPR accountability requirements, DPIA processes and the kind of evidence trail your legal team will actually ask for.
Switch models without switching policies
Your tokenization rules, access controls and compliance settings stay in place regardless of which LLM you use. No vendor lock-in. No rework when the market moves.
Built for EU data standards
Designed around GDPR principles and European data protection requirements from the ground up. Deploy AI in Europe without compromising on compliance or control.
Turn governance into an accelerator
Organizations that operationalize trust, transparency and AI security see significantly better adoption and business outcomes. Proxy AI One gives your teams a safe path to yes.
Data protection
Designed for organizations that take data protection seriously.
European data protection law sets a high standard and for good reason. Proxy AI One is built to help organizations meet that standard without sacrificing access to modern AI capabilities.
Proxy AI One is currently in active development. Our architecture is designed around GDPR principles and EU data protection requirements from the ground up. We are working toward formal compliance documentation and will share updates with early access partners as they become available. If you have specific compliance requirements, we are happy to discuss them directly.
Our goal is to help you reduce exposure, accelerate audit readiness and improve control and visibility across every AI workflow. Not as a promise, but as a measurable outcome.
Core principles
- Data minimization by design — only what's necessary leaves your perimeter
- No training on your data — tokenized prompts cannot be used to improve external models
- Audit trail support — visibility into what was processed, when, and how
- Configurable retention policies — aligned with your internal data governance
- EU-first infrastructure options — processing within European data boundaries
- Custom classification rules — adapt to sector-specific regulatory requirements
Early access
Be among the first to use Proxy AI One.
We're onboarding a select group of organizations before public launch. Leave your details and we'll be in touch.
Proxy AI One acts as an intelligent intermediary between your systems and external AI models. Before any prompt leaves your environment, it identifies and tokenizes sensitive information — replacing it with opaque placeholders. After the model responds, the original context is restored. Your application gets a full, coherent answer. The external model never sees your real data.
No — the opposite. Proxy AI One is designed to enable AI adoption by removing the compliance and security blockers that cause organizations to restrict access in the first place. Your teams can use GPT-4, Claude, Mistral, or other frontier models with confidence, because the sensitive data exposure problem is handled at the infrastructure level.
Yes. Proxy AI One is model-agnostic by design. It acts as a proxy layer that can route sanitized prompts to any LLM endpoint — OpenAI, Anthropic, Mistral, Azure OpenAI, and others. You can also switch or add providers without changing your tokenization configuration.
Proxy AI One is built specifically for organizations that need to meet security, compliance, and governance requirements before deploying AI. This means audit trail support, configurable policies, role-based access controls, and infrastructure options that fit enterprise environments — including EU-hosted deployments.
Yes. You can define custom entity types, extend the default PII detection, and configure domain-specific rules — for example, tokenizing internal project codes, product names, or client identifiers that wouldn't be caught by generic detectors. The policy layer is designed to adapt to your data model.
By ensuring that sensitive data never leaves your organization in readable form, Proxy AI One helps reduce the surface area of compliance risk. The system supports data minimization principles, configurable retention, and processing transparency — relevant to GDPR and sector-specific frameworks. [Note: specific compliance certifications are in progress — contact us for the current status.]
Proxy AI One is currently in late development, preparing for a controlled early access launch. We're onboarding a limited number of organizations to test the product in real environments before general availability. If you're interested, the waitlist above is the best way to get in early.
No. Tokenization ensures that real sensitive data never reaches any external model. What the LLM processes are anonymous tokens, not your actual data. Nothing you send through Proxy AI One is used to train, fine-tune or improve any external model.
Proxy AI One is designed to support accountability under GDPR. It maintains a full audit trail of what was processed, when and under which policies. This means you can respond to auditor requests, conduct DPIAs and demonstrate privacy by design without scrambling for evidence after the fact.
Because your real data never reaches the external model, a breach at the LLM provider level does not expose your sensitive information. The tokenized data they hold has no value without the mapping that stays inside your organization.
Get early access
Ready to adopt AI
without the risk?
Join the waitlist today. We're onboarding early access partners before the public launch.