Remove PII from documents, audio recordings, and images to protect your customers’ sensitive data and comply with global privacy regulations like the GDPR, CPRA, HIPAA, and more.
Private AI can be integrated into any cloud infrastructure or database to identify and redact PII to help with critical data discovery and data classification projects. We deploy as a Docker container so your data never leaves your environment and is never shared with an external party (including Private AI).
Built by experts from:
Built by experts from:
Private AI can also detect and remove PII in a range of different document types, such as PDF, DOCX, and many more. In addition to scanning text fields, Private AI also scans embedded graphics for complete coverage. After PII has been identified, a copy of the document with all PII blurred can be created.
Contact us for documentation and more information.
Private AI blurs faces and personally identifiable text (eg. licence plates, credit card details, addresses, and more) from images to protect your customer data and comply with privacy regulations. View the full list of entities we capture.
Contact us for documentation and more information.
Private AI removes PII from audio recordings with customizable bleeping for basic data protection. For compliance with privacy regulations, like the GDPR, Private AI also offers voice and tone distortions to effectively de-identify audio recordings.
Compatible with 10+ audio formats, including WAV and MP3. Contact us for documentation and more information.
Fill out the form below and we’ll send you a free API key for 500 calls (approx. 50k words). No commitment, no credit card required!
Expand the categories below to see which languages are included within each language pack.
Note: English capabilities are automatically included within the Enterprise pricing tier.
French
Spanish
Portuguese
Arabic
Hebrew
Persian (Farsi)
Swahili
French
German
Italian
Portuguese
Russian
Spanish
Ukrainian
Belarusian
Bulgarian
Catalan
Croatian
Czech
Danish
Dutch
Estonian
Finnish
Greek
Hungarian
Icelandic
Latvian
Lithuanian
Luxembourgish
Polish
Romanian
Slovak
Slovenian
Swedish
Turkish
Hindi
Korean
Tagalog
Bengali
Burmese
Indonesian
Khmer
Japanese
Malay
Moldovan
Norwegian (Bokmål)
Punjabi
Tamil
Thai
Vietnamese
Mandarin (simplified)
Arabic
Belarusian
Bengali
Bulgarian
Burmese
Catalan
Croatian
Czech
Danish
Dutch
Estonian
Finnish
French
German
Greek
Hebrew
Hindi
Hungarian
Icelandic
Indonesian
Italian
Japanese
Khmer
Korean
Latvian
Lithuanian
Luxembourgish
Malay
Mandarin (simplified)
Moldovan
Norwegian (Bokmål)
Persian (Farsi)
Polish
Portuguese
Punjabi
Romanian
Russian
Slovak
Slovenian
Spanish
Swahili
Swedish
Tagalog
Tamil
Thai
Turkish
Ukrainian
Vietnamese
Testé sur un ensemble de données composé de données conversationnelles désordonnées contenant des informations de santé sensibles. Téléchargez notre livre blanc pour plus de détails, ainsi que nos performances en termes d’exactitude et de score F1, ou contactez-nous pour obtenir une copie du code d’évaluation.
Number quoted is the number of PII words missed as a fraction of total number of words. Computed on a 268 thousand word internal test dataset, comprising data from over 50 different sources, including web scrapes, emails and ASR transcripts.
Please contact us for a copy of the code used to compute these metrics, try it yourself here, or download our whitepaper.