LTIMindtree Achieves a Double-Digit Performance Improvement Using SigOpt for AI Model Tuning

9 Pages

LTIMindtree used the SigOpt Intelligent Experimentation Platform to fine-tune AI models for call summarization in telecom contact centers. By automating hyperparameter optimization and leveraging SigOpt’s multimetric features, LTIMindtree reduced inference time by up to 63% and improved summarization accuracy. This cut chatbot response time from 16.75 to 6.86 seconds and enhanced agent productivity. The platform also enabled better model selection and operational efficiency, leading LTIMindtree to offer AI model tuning as a product for contact centers.

Join for free to read

Case Study P.S. 20 sees double-digit growth

Case Study Delivering Double Digit Growth With Sage 200

Case Study LTIMindtree Personalization Effectiveness Model

Case Study DreamBox Learning Brings Double Digit Gains

More from Intel

Case Study Novelis Taps HPC and AI to Accelerate Beverage Can Design…

Guide Adapt and Transform - Infrastructure to Drive Revenue Potential…

Case Study Meituan Accelerates Vision AI Inference Services and Optimizes…

Case Study Zoho is Optimizing and Accelerating Video AI Workloads

Case Study

LTIMindtree Achieves a Double-Digit Performance Improvement Using SigOpt for AI Model Tuning

LTIMindtree Achieves a Double-Digit Performance Improvement Using SigOpt for AI Model Tuning

You Might Also Like

More from Intel