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AI Kick Start

PII protection layers before sensitive data reaches AI.

AI Kick Start designs PII protection layers for AI workflows that touch customer records, employee data, finance files, case notes, or private documents. The goal is to detect, redact, replace, or constrain sensitive information before a model or automation step can mishandle it.

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Cloak web landing page showing private document AI on synthetic twins for Acrobat, Word, and Excel

PII risk mapping

We identify where personal information appears, who can access it, which tools process it, where it leaves the business, and what records are needed.

Detection and redaction

Names, emails, phone numbers, addresses, identifiers, account details, dates, and sensitive context can be detected and masked.

Synthetic placeholders

Sensitive fields can be replaced with consistent placeholders so the workflow still makes sense without exposing real people.

Local-first privacy controls

Some workflows should run detection, redaction, and review inside your boundary before any cloud model or external API is involved.

Audit trails and review gates

A defensible privacy layer records what was detected, redacted, processed, reviewed, and actioned.

Integration into real workflows

PII protection can sit in front of document AI, RAG, automations, support triage, CRM updates, email drafting, and reporting.

Cloak software

A Rust/Tauri desktop app for private document AI workflows.

The desktop app runs the protected workspace locally. A user can open a document, inspect the transformed view, review the ledger, ask questions, and receive restored answers while the raw mapping stays on the machine.

PDF/DOCX/XLSXsupported document workflow patternsLocal ledgermapping stays inside the trust boundaryRust + Taurinative desktop packaging for Windows-style local workflows
Talk about PII-safe AINative app builds
Cloak native desktop workspace showing chat, trust boundary status, ledger count, and local restoration message with demo personal name redacted
Native desktop capture from the local Rust/Tauri Cloak app. Demo text is redacted where needed for public publishing.

How Cloak works

Visitors can see the privacy workflow, not just read the promise.

The screenshots show the public landing page, the local processing pipeline, and the native workspace surface that explains the trust boundary.

Cloak landing page showing private document AI on synthetic twins with Acrobat, Word, and Excel support

Landing page

Start with the trust boundary

The public web surface explains that original files stay inside the local boundary while synthetic twins are used for model reasoning.

Cloak processing pipeline showing sidecar health check, extract document, detect entities, generate synthetic twins, and build ledger

Processing pipeline

Extract, detect, replace, ledger

The pipeline verifies the sidecar, extracts the document, detects entities, generates synthetic twins, and builds the restoration ledger.

Cloak desktop workspace showing redaction boundary, chat answer, ledger count, and local restoration controls

Desktop app

Ask questions without exposing originals

The workspace shows chat, transform, ledger, audit, and canvas surfaces for a document session, with restored answers rebuilt locally.

Implementation pattern

The same privacy layer can sit in front of chat, RAG, or document automation.

AI Kick Start uses this pattern for privacy-sensitive teams that want AI assistance without casually pasting customer, patient, staff, or financial data into a public model.

  1. 01

    Define the boundary

    Decide what must stay on-device, what can be transformed, and which systems or users are allowed to see raw values.

  2. 02

    Detect and replace

    Run deterministic rules, entity detectors, and domain-specific checks before the model call, then replace sensitive spans with stable placeholders.

  3. 03

    Reason over the safe view

    Use the transformed projection for chat, summarisation, RAG, extraction, or workflow automation while keeping the mapping ledger local.

  4. 04

    Restore and audit

    Restore answers locally, log what changed, and keep a review trail for compliance, privacy, and operational trust.

Related services

Plan the surrounding system.

Most useful AI projects connect more than one service. These are the next pages worth comparing before you scope the first build.

Secure Document AI

Use PII protection inside sensitive document workflows.

View service

RAG & Knowledge Systems

Protect private source material before building knowledge AI.

View service

On-Premise AI

Run sensitive AI workloads inside a controlled local environment.

View service

AI Training Programs

Teach teams what not to upload and how to review AI output.

View service

FAQ

Common questions before the first call.

What is PII protection for AI?

It is a set of controls that detects, redacts, replaces, or limits personal information before AI tools process sensitive business data.

What counts as PII?

PII can include names, addresses, phone numbers, emails, account numbers, identifiers, health details, financial records, and information that can identify a person.

Can you redact before using ChatGPT or Claude?

Yes. A safer workflow can create a redacted working copy before any cloud AI tool sees the content.

Does PII protection replace privacy governance?

No. It supports governance, but the business still needs approved tools, access rules, retention rules, review owners, and staff training.

Start here

Build your AI roadmap.

Bring one workflow, one growth problem, or one team that needs to get moving. We will map the first useful system.

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