01 / PREPARE
Prepare your input locally.
Prepare your text in a local workspace, separate from the AI tool. Review it before sharing the masked version.
01 / A privacy tool in development
Share the context.
Keep private details private.
We’re building a privacy layer that masks sensitive information before it reaches AI tools — then uses a local mapping to restore those details in the response.
Designed for local processing across AI workflows.
THE PRIVACY LAYERIllustrative example
Original prompt
Draft a brief email to Alex Morgan at [email protected] about the outage on 192.168.1.20.
Three details to mask: name, email address, private IP address.
Planned workflow: a local replacement mapConcept
This example uses fictional data. It does not analyze your own text.
01 Local processing by design02 Consistent, reversible placeholders03 Review before sharing
A useful prompt.
Less accidental disclosure.
Documents, support messages, and error logs can contain sensitive details that an AI tool does not need. The planned privacy layer gives you a chance to review those details before sharing.
02 / REVIEW
Review what you share.
Review the detected details and choose what to mask. Consistent placeholders help preserve the relationships within your text.
03 / RESTORE
Restore the original details.
Use the mapping on your device to restore unchanged placeholders in the response. Read the restored answer locally.
This describes the intended workflow. Automatic detection can miss sensitive details; review remains part of the design.
02 / Product direction
Start focused.
Build for a global audience.
TalhaBuild is developing a privacy layer for a global audience. The product is currently in the concept and design stage; these are the planned next steps.
No launch date announcedA focused text prototype
Build local detection for email addresses, phone numbers, IP addresses, and selected API key formats, with a review screen and a consistent replacement map.
Context and language coverage
Evaluate local models for names and addresses, starting with English and Turkish. Explore country-specific identifiers as coverage grows.
More AI tools
Explore browser, desktop, and developer integrations. Assess optional cloud-assisted detection against the project’s privacy requirements before deciding whether to offer it.
03 / Questions
About the project.
Is the product available now?
Not yet. The product is in the concept and design stage. This page introduces the planned workflow through an interactive example using fictional data.
How does masking affect AI responses?
Consistent placeholders help preserve relationships between people and other details. Masking can still affect the answer when your request depends on the original value. Placeholders that the AI changes or omits may not be restorable.
Will my original text leave my device?
The planned default is local processing: the AI tool would receive the masked text. If a cloud-assisted option is introduced, it would require your explicit choice and clearly explain what information leaves your device.
Does masking make text fully anonymous?
No. Automated detection may miss information, and the remaining context may still identify someone. The goal is to reduce accidental disclosure, with a review step before sharing.
Who is building this?
Talha is building the project under TalhaBuild. Feedback from people who use AI in their everyday work will help shape the first prototype.
Help shape the first prototype
Where would this fit in your workflow?
Tell Talha which kinds of information you want to mask and which AI tools you use. Please use fictional examples rather than private data.