Privacy Twins: language-based privacy for a language-based threat

The process

Real data in. Privacy Twin out. Real answer back.

The full process happens in under 50 milliseconds on-device. Zero learning curve. The moment you type it, we protect it. The moment the answer arrives, your real values are stitched back in.

You type normally

Your team uses AI exactly as they always have: no changed workflows, no prompts to remember, no extra steps.

Privacy Twins swap in

PrivacyPal detects every sensitive entity and replaces each one with a statistically identical synthetic equivalent before the prompt is sent.

You see real answers

The AI's response uses the synthetic data. PrivacyPal re-injects your real data so you see the answer with your actual names, figures, and context. Only for you.

01 · The science

Statistically identical

What makes a Privacy Twin statistically identical?

A simple data mask replaces "Acme Corp" with "COMPANY." A Privacy Twin replaces it with "Nexus Solutions," a synthetic entity with consistent industry type, revenue range, legal structure, and relationship context.

The AI model receives a prompt that is structurally and semantically equivalent to the original. It reasons about the same relationships, dependencies, and patterns, and returns an answer that is just as accurate and useful as if it had seen the real data.

Your real data never leaves your device. The synthetic data has zero information value to anyone who intercepts it.

Structural equivalence Semantic preservation Relationship consistency Domain-appropriate values Zero real data exposure

	Your real data	Privacy Twin sent to AI
Client	Acme Corp	Nexus Solutions
Contact	Sarah Mitchell	Dana Brooks
Deal value	$2,400,000	$1,800,000
Document	Q3-Pipeline.xlsx	Doc-7291.xlsx
Project	Project Orion	Project Kepler

AI responses are equally accurate for both columns. Only you see the real version.

Use cases

Every department. Every AI surface. Governed.

Privacy Twins run silently across every role and every AI: ChatGPT, Claude, Gemini, Copilot, Claude Code, MCP agents. Same governance plane, every workflow.

Sales & business development

Client data & deal intelligence

Sales teams frequently ask AI to analyze CRM data, draft proposals, and summarize pipeline reports, all containing real client names and deal values.

Ask AI to "Summarize the Acme Corp deal risks" and Privacy Twins ensure Acme Corp and all associated contacts are never exposed, while the AI's risk analysis is just as accurate.

Engineering & development

Source code & API secrets

Developers routinely paste proprietary code, architecture diagrams, and API specifications into AI coding assistants. This is the single largest IP leak vector in modern businesses.

Paste a function containing real database credentials or proprietary logic into Copilot: Privacy Twins replace sensitive identifiers before any data is transmitted.

Legal & compliance

Contracts & NDA-protected content

Legal teams ask AI to draft responses, summarize agreements, or check contract language, often pasting actual terms that can't leave the firm.

Upload a client NDA for AI review: Privacy Twins replace all party names, dollar amounts, and identifying terms before the document is processed.

Finance & accounting

Financial models & forecasts

Finance teams use AI to spot anomalies, generate narratives, and model scenarios, often uploading actual budget spreadsheets with real revenue data.

Upload a Q3 financial model: Privacy Twins scale all figures proportionally so AI analysis is identical without exposing actual revenues or targets.

HR & people operations

Employee data & performance

HR teams ask AI to draft performance reviews, analyze compensation data, or summarize feedback, all highly sensitive and personally identifiable.

Ask AI to "Draft a performance review for Jane Smith": Privacy Twins replace the real employee name, salary, and department before the request is processed.

Executive & strategy

Board materials & M&A data

Executives use AI to prepare board presentations, analyze competitive intelligence, and draft acquisition rationale, some of the most sensitive data in any organization.

Paste a confidential term sheet into Claude for analysis: Privacy Twins replace all entity names, valuations, and deal structures before any data leaves the device.

Technical performance

Built for accuracy at scale

Privacy Twins aren't just a privacy shield; they're engineered to preserve the quality of your AI outputs.

100%

Redaction accuracy. In independent testing, zero real data entities passed through in over 10,000 test prompts.

<50ms

Twin generation latency. The swap happens entirely on-device and completes before the prompt is ready to send.

98.6%

Response equivalence. AI responses using Privacy Twins are rated equivalent in accuracy to responses using real data.

∞

Unique twin pairs. Each Privacy Twin is generated fresh per session: no reuse, no pattern inference possible.

02 · Re-injection

The round trip

You always see your real data in the answer

The AI's response comes back using the Privacy Twin names and values. PrivacyPal then re-injects your actual data on your device, so the answer you read is fully in context with your real situation.

No manual translation. No cross-referencing a "fake" answer back to reality. You get the full power of AI plus the full context of your real business, simultaneously.

Automatic re-injection

Real values are restored automatically on response receipt.

Highlighted in your interface

So you always know what was protected.

Every platform, identically

Works across all supported AI platforms identically.