Every breakthrough begins with a bold question. At ClairVault, ours was: Why should data security limit innovation? Frustrated by the constant trade-off between protecting sensitive information and utilizing it to its fullest potential, we brought together a team of forward-thinking experts in cryptography and machine learning. Embracing the cutting-edge realm of vector embedding encryption, we set out to make data both secure and remarkably accessible. Today, we're passionate about empowering businesses to unlock their data's true potential, fueling innovation securely and without limits.
Identify patients with similar medical histories or symptoms without exposing sensitive information. Encrypted data allows healthcare providers to compare patient records while maintaining privacy.
Perform risk assessments by comparing encrypted customer profiles. Maintain the ordering of risk levels, enabling financial institutions to categorize clients by risk without exposing raw financial data.
Match customers with similar purchase histories to provide personalized recommendations. By maintaining the relative order of encrypted purchase data, retailers can offer targeted suggestions while keeping customer information secure.
Search for legal cases with similar characteristics using encrypted data. The technology allows law firms to maintain confidentiality while still being able to identify related cases by comparing encrypted attributes.
Identify insurance claims with similar characteristics for quicker processing and risk evaluation. The approximate ordering ensures privacy while maintaining the utility of encrypted claims data for comparison.
Group students with similar performance metrics for personalized learning plans. The technology ensures that student records remain private while allowing for meaningful comparisons of academic performance.
Compare suppliers based on performance metrics to optimize procurement decisions. Using encrypted data, manufacturing firms can maintain the ranking of suppliers while keeping sensitive performance data confidential.
Compare features within encrypted datasets to identify similarities for machine learning model development. This enables the use of sensitive datasets for AI without revealing the raw data, while still maintaining feature relationships.
