About Us

Started by privacy and machine learning experts from the University of Toronto, Private AI’s mission is to build the privacy layer for software. Our team strives for product perfection, while building a culture imbued with generosity, continuous teaching, learning and feedback, and celebrations of each others’ successes. 

We share a passion for responsible innovation and a mutual bond over our imposed love of pie (only fitting, with a company acronym like “PAI”).

Built by experts from:

Built by experts from:

By the Numbers

0 K +

Hours Building our PII Detector

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Words Processed Per Month

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Countries

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Pies Consumed

Our Co-Founders

Patricia Thaine
Co-Founder & CEO

Patricia Thaine

Co-Founder & CEO, Private AI

Patricia Thaine is a Computer Science PhD Candidate at the University of Toronto and a Vector Institute alumna doing research on privacy-preserving natural language processing, with a focus on applied cryptography. She also does research on computational methods for lost language decipherment. Patricia is a recipient of the NSERC Postgraduate Scholarship, the RBC Graduate Fellowship, the Beatrice “Trixie” Worsley Graduate Scholarship in Computer Science, and the Ontario Graduate Scholarship. She has eight years of research and software development experience, including at the McGill Language Development Lab, the University of Toronto’s Computational Linguistics Lab, the University of Toronto’s Department of Linguistics, and the Public Health Agency of Canada.

Pieter Luitjens
Co-Founder & CTO

Pieter Luitjens, MEng

Co-Founder & CTO, Private AI

Pieter Luitjens has a Bachelor of Science in Physics and Mathematics and a Bachelor of Engineering from the University of Western Australia, as well as a Masters from the University of Toronto. He worked on software for Mercedes-Benz and developed the first deep learning algorithms for traffic sign recognition deployed in cars made by one of the most prestigious car manufacturers in the world. He has over 10 years of engineering experience, with code deployed in multi-billion dollar industrial projects. Pieter specializes in ML edge deployment & model optimization for resource-constrained environments.

Management Team

John Stocks
VP of ML Partnerships

Kory Fong
VP of Engineering

Michael Young
VP of Product

Toks Olaoluwa
VP Enterprise Sales

Our Partners

Join Our Team

See the listings below to discover our current openings.

Download the Free Report

Request an API Key

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!

Language Packs

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

Rappel

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.

99.5%+ Accuracy

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.