Client
The client is a global semiconductor company specializing in intelligent power and sensing technologies for the automotive and industrial sectors. With a worldwide workforce of over 25,000 and a broad manufacturing footprint, the organization delivers differentiated solutions that drive electrification, automation, and sustainable infrastructure worldwide.
As digital transformation accelerates across the industry, the client sought to enhance customer experience and operational efficiency by deploying conversational AI-powered chatbots within its contact centers. However, ensuring the accuracy and reliability of large language models (LLMs) presented new challenges, demanding a more objective, scalable approach to risk assessment and chatbot evaluation.
Market trends in the semiconductor industry
The global semiconductor industry is entering a high-growth phase, driven by surging demand for generative AI chips, advanced sensing technologies, and the rapid expansion of data centers. As AI and automation reshape manufacturing, supply chain management, and customer service, semiconductor leaders must ensure their digital platforms, including AI-powered chatbots, are accurate, efficient, and scalable.
Intense competition, ongoing talent shortages, and rising customer expectations push organizations to adopt automated, objective methods for deploying and evaluating advanced AI models.
Business Challenges
- Manual, time-consuming risk discovery: Identifying risks in new chatbot projects required significant effort from senior management, slowing deployment and diverting resources from strategic priorities.
- Subjective LLM response evaluation: The quality and reliability of chatbot outputs depended heavily on individual tester expertise, resulting in inconsistent feedback and a lack of standardization.
- Difficulty ensuring chatbot reliability at scale: Managing multiple chatbots across the contact center made it challenging to maintain consistent response quality, undermining user trust and customer experience.
LTIMindtree solution
LTIMindtree partnered with the client to automate and standardize LLM evaluation across their contact center chatbots, delivering greater confidence and efficiency in AI-powered customer interactions.
Automated, objective evaluation using advanced LLM evaluation techniques
Flexible governance using watsonx integration
Comparative evaluation for accuracy and risk
Streamlined risk discovery and reporting
Business benefits
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Increased confidence in chatbot performance
Automated, objective evaluation enabled the client to select and deploy the most reliable LLMs, boosting trust among internal teams and end-users.
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Faster, more efficient model evaluation
Automated workflows and transparent governance reduced manual review cycles, accelerating chatbot deployment and freeing up senior management for higher-value work further saving 30% of OpEx.
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Consistent, data-driven risk identification
Comparative analysis against human and alternative LLM outputs enabled proactive issue detection, reducing the risk of errors and improving customer experience.
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Scalable quality management
The solution supported rapid expansion and ongoing monitoring of multiple chatbots across the contact center, ensuring consistent response quality as operations grew.
Conclusion
By partnering with LTIMindtree to automate and standardize LLM evaluation, the client transformed chatbot quality management across their global contact centers. The solution delivered faster, more reliable model assessment, reduced operational overhead, and enabled consistent, trustworthy customer interactions.
With scalable, data-driven workflows now in place, the client is well-positioned to expand its AI initiatives, drive continuous improvement, and strengthen their leadership in customer experience and semiconductor innovation.
Take the lead in your AI journey.
With BlueVerse by LTIMindtree, unlock consistent, high-quality customer experiences through automated LLM evaluation, scalable workflows, and continuous AI optimization.
