Unlocking Engineering Productivity-old

Client

The client is a global European semiconductor manufacturer specializing in essential automotive, industrial, and consumer electronics components. With over 100 billion units shipped annually and a growing presence in wide-bandgap technologies such as Silicon Carbide (SiC) and Gallium Nitride (GaN), the company is foundational in enabling next-generation hardware systems worldwide.

As the organization expanded its engineering footprint, teams faced increasing difficulty accessing the proper technical knowledge at the right time. With rising documentation volume, compliance needs, and project complexity, the client needed a modern, AI-powered solution to streamline research, reduce manual effort, and improve engineering productivity.

Market trends in the semiconductor industry

The global semiconductor industry is entering a new growth phase, driven by demand for AI-optimized chips, high-performance computing, and next-gen automotive systems. As the market rebounds from cyclical slowdowns, key players invest in wide-bandgap technologies like SiC and GaN to support energy-efficient, high-power applications.

Semiconductor manufacturers must manage increasingly complex product portfolios, faster design cycles, and stricter compliance requirements to stay competitive. Engineering teams are under pressure to accelerate innovation while navigating fragmented documentation and legacy knowledge silos. This is fueling the adoption of agentic AI-powered knowledge management solutions that can help streamline technical research, enhance traceability, and reduce time to insight across product lifecycles.

Business Challenges

• Inefficient access to historical engineering knowledge: Engineers struggled to retrieve relevant information from past project documents for risk assessment, resulting in delays, duplicated effort, and slower project initiation.
• No centralized system for information retrieval: Finding the appropriate content for each stakeholder at the right time was often inconsistent and time-consuming, which hindered productivity and raised coordination overhead.
• Overdependence on subject matter experts (SMEs): Complex terminology and non-standard documentation practices led to high reliance on specialized knowledge, making it challenging to scale engineering reviews or onboard new team members efficiently.
• Low ROI on documentation efforts: Teams found it hard to justify the time and cost of creating and maintaining technical documents that weren’t easily searchable, leading to underutilization of valuable organizational knowledge.

LTIMindtree Solution

To modernize risk assessment and advance risk management automation, LTIMindtree implemented a customized GenAI solution tailored to the client’s engineering environment. The platform combined semantic search, contextual risk analysis, and generative capabilities to improve speed, accuracy, and knowledge accessibility.

• Deployed a GenAI risk assessment chat assistant for engineering : Built using LTIMindtree’s BlueVerse platform, the assistant allowed users to query past project documentation in natural language, dramatically reducing the time spent navigating technical archives for risk evaluation.
• Implemented semantic search for contextual risk identification : GenAI models were fine-tuned to extract and correlate risk patterns from historical documents, helping teams quickly identify potential issues related to quality deviations, regulatory noncompliance, and supplier performance for specific engineering projects.
• Integrated Retrieval Augmented Generation (RAG) for answer reliability : To ensure accuracy and prevent AI hallucinations, a RAG framework was used to ground all responses in verifiable source content, boosting trust in system-generated insights across technical and compliance teams.
• Customized a self-hosted Llama 3.1 model to reflect domain language : The model was trained on the company’s internal vocabulary and documentation styles, making search outputs more context-aware and reducing reliance on niche subject matter expertise for interpretation.

Benefits

• 50% reduction in time spent on risk assessment : The client had access to relevant information in minutes rather than hours, accelerating project evaluation and early-stage decision-making.

• 25% savings in documentation-related costs : Automated knowledge retrieval and reduced dependency on SMEs lowered the time and resources spent maintaining technical archives.

• Improved engineering productivity : With intuitive search and GenAI-powered summarization, teams spent less time interpreting dense technical material and more time executing high-value work.

• Enhanced knowledge accessibility across roles : The solution democratized access to institutional knowledge, enabling cross-functional teams, including quality, compliance, and engineering, to extract insights without needing deep domain expertise.

• Increased confidence in AI-generated responses : RAG ensured responses were traceable to actual documents, enabling auditable insights and greater trust among technical reviewers.

Conclusion

The client dramatically streamlined risk assessment and knowledge retrieval by embedding GenAI-driven search, contextual risk analysis, and document intelligence into their engineering workflows. The transformation delivered faster project initiation, reduced operational costs, and empowered teams to make data-driven decisions confidently.

With this foundation, the client can scale AI-enabled insights across broader product development and compliance initiatives, accelerating innovation while maintaining traceability and control.

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