A leading provider of contract life-cycle management solutions, focused on streamlining and automating end-to-end contract processes.

The client’s operations spanned multiple cloud zones, each storing vast amounts of sensitive customer information critical for machine learning initiatives. Protecting this data while complying with privacy regulations was essential for the client’s AI-driven contract management solutions. Existing manual data sanitization processes were complex, error-prone, and inefficient, leading to delays and potential risks of data breaches.

The client, with Turing's support, implemented a comprehensive AI-driven data sanitization service to address the challenges of securing sensitive customer data:

• Custom NER models: Turing developed custom named-entity recognition (NER) models to accurately detect and sanitize sensitive data elements, improving the precision of data sanitization. These AI models were trained and deployed using Python to offer flexibility and powerful computational capabilities.
• Scalable architecture: The solution was built on a scalable architecture using Kubernetes and AWS EKS, enabling the efficient processing of over 114 million paragraphs of data daily. The integration of PostgreSQL as a secure database for sanitized data ensured compliance with privacy regulations and enhanced operational efficiency.

• 12% revenue growth: The AI-driven data sanitization service enabled the client to improve data processing and billing, leading to a significant increase in revenue.
• 25% improvement in customer satisfaction: Secure data handling and improved data processing capabilities led to higher customer satisfaction.
• 30% increase in platform scalability: The scalable architecture allowed the system to handle large volumes of data efficiently, supporting the client’s AI initiatives and enhancing overall performance.