CamoText provides an offline, AI‑agnostic privacy layer that automatically detects and masks over 20 types of personal and confidential data—including names, IDs, financials, and locations—before the content is sent to any AI model. By operating locally with zero data retention and metadata‑free output, it enables law firms, medical practices, and public sector organizations to meet GDPR, HIPAA, and FISMA compliance while preserving full control over sensitive information.
Funding
Funding not disclosed
Founders
Product
Problem
CamoText addresses the risk that personal identifying information (PII) and other confidential data can be exposed when users submit content to AI models, especially when those models operate in cloud environments that may retain data or metadata.
Solution
CamoText offers an offline, privacy‑first engine that masks PII before any data is sent to an AI system. The tool automatically detects more than 20 data types—including names, identification numbers, financial details, and locations—and allows users to apply custom tags, reverse false positives, and selectively anonymize content at the file or folder level. By ensuring zero data retention and metadata‑free output, CamoText enables compliant workflows that meet GDPR, HIPAA, and FISMA requirements, allowing organizations to safely leverage any downstream AI service without compromising privacy.
Target Audience
Primary customers are law firms, medical practices, and public sector agencies that must protect sensitive information and comply with strict data‑privacy regulations.
Features
- Offline processing that keeps all data on the user’s device, eliminating cloud‑based retention risks
- Automatic detection of 20+ PII categories with high accuracy
- Custom tagging and reversible anonymization to fine‑tune what information is masked
- Support for single‑file and bulk folder anonymization workflows
- Metadata stripping to produce clean, privacy‑safe outputs
- Optional de‑anonymization using locally stored keys for controlled data recovery