Obfys brings AI-powered redaction directly into your hands whether on mobile or desktop. Detect and remove sensitive information from emails, attachments, PDFs, Word documents, Excel files, images, and more before sharing or storage. Built for professionals and teams handling confidential data, Obfys helps reduce manual redaction time by up to 75% while improving security, compliance, and workflow efficiency.
Just launched Obfys - an AI-powered redaction platform built for protected sensitive data at scale.
Obfys helps professionals securely identify and remove sensitive information from emails, attachments, PDFs, Word documents, Excel files, images, and more before sharing. Our goal was simple: make enterprise-grade redaction dramatically faster, easier, and more accessible without disrupting existing workflows.
With AI-assisted detection, bulk processing, Office integrations, metadata removal, and real-time review tools, teams can reduce manual redaction time by up to 75% while improving compliance and reducing human error.
Would love feedback from the Product Hunt community on:
* Outlook and Office add-in workflows
* AI-assisted compliance tooling
* Enterprise UX
* Redaction accuracy and review flows
* Features you’d want in a secure collaboration/redaction platform
Thanks for checking out Obfys 🚀
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Client privacy and data leaks are major liabilities for modern businesses, especially when sharing long documents or email threads. A tool that handles deep redaction natively and locally sounds like a great privacy-first utility. Does it rely purely on client-side regex matching or use a local model for contextual entity detection?
@rivra_dev We use a mixture of techniques. Limited regex matching is done server side, we also use a layered approach for entity detection involving both local and external models.
DocEndorse AI Agent for Microsoft Teams
Client privacy and data leaks are major liabilities for modern businesses, especially when sharing long documents or email threads. A tool that handles deep redaction natively and locally sounds like a great privacy-first utility. Does it rely purely on client-side regex matching or use a local model for contextual entity detection?
DocEndorse AI Agent for Microsoft Teams
@rivra_dev We use a mixture of techniques. Limited regex matching is done server side, we also use a layered approach for entity detection involving both local and external models.