Firm's customers range from financial institutions to conversational AI companies
Private AI, a developer of privacy-preserving machine learning and natural language processing tools, announced that it has secured $3.15 million in seed funding to improve their product offering, expand the team, and accelerate their acquisition of domestic and international customers.
Private AI’s customer base now ranges from startups to multi-billion-dollar companies, including financial institutions and conversational AI companies.
M12 Principal Priyanka Mitra said: “Companies of all sizes are under pressure to comply with customer data governance regulations and to protect sensitive data. In parallel, business digitization is accelerating, and more customer data unlocks more insights and enhanced AI/ML model training capabilities. We’re thrilled to support the world-class team at Private AI as they augment their customers’ data redaction and pseudonymization capabilities, improving enterprise security posture without sacrificing business intelligence.”
Joining M12 and Forum Ventures in the $3.15 million seed round is pre-seed investor Differential Ventures, along with new investors Shasta Ventures, Hyperplane Venture Capital, and Parliament Angels.
Patricia Thaine, CEO of Private AI, said:
“This round of funding will help us provide organizations and their developers with world-leading easy-to-integrate tools so they can excel in this post-GDPR world.”
“Our partners at M12 and Forum Ventures both have deep expertise in B2B SaaS and developer-focused tools, and their investment and counsel will be key to fuelling our growth.”
What distinguishes Private AI from similar offerings is how easily and securely the company’s software can be implemented. It only takes three lines of code and operates as a black box. Customer tests have shown that Private AI’s system outperforms those of Amazon and Google by significant margins, and are able to operate directly within their clients’ workflows and infrastructure, which prevents sensitive data from being shared outside clients’ systems.
The company’s state-of-the-art AI models are able to hit greater than 99% accuracy in identifying and redacting personal data across more than fifty different entities (ex. name, address, blood type, zodiac sign, credit card number, etc.) in seven different languages.
“Many software engineering teams don’t have strong procedures or processes in place to identify and protect this data,” says Thaine. “Instead, companies often rely on ineffective and outdated systems that don’t work well on messy, real world data, or simply trust their employee onboarding paperwork commitments to protect them.”