Development of AI-powered RAG knowledge base for EMC data

Customer: Automotive Industry Company

Project: Development of AI-powered RAG knowledge base for EMC data

Product Description

Syntronic collaborated with an automotive company to build an AI-powered Retrieval-Augmented Generation (RAG) knowledge base for electromagnetic compatibility (EMC) data. The solution was designed to transform large volumes of unstructured compliance information into a searchable, intelligent system tailored for engineering teams. By applying advanced large language model (LLM) training, semantic search, and domain-specific knowledge embeddings, Syntronic enabled engineers to access accurate EMC requirements instantly, improving productivity and compliance reliability.

Scope

Under a dedicated engagement model, Syntronic assumed responsibility for the entire system design and deployment. The assignment included data migration from legacy systems, cleaning and normalization of EMC records, and the development of a robust RAG architecture trained on automotive EMC regulations and technical standards.

Close collaboration with the customer’s engineering and IT teams ensured seamless integration of the knowledge base with existing workflows, including CAD tools and compliance platforms. Syntronic also established processes for ongoing updates, maintenance, and scalability to support the customer’s future product development and regulatory needs.

Results

The project significantly improved the customer’s EMC data management and compliance workflows. Engineers reduced data retrieval time by up to 80 percent, freeing resources for innovation and accelerating product development cycles. The solution streamlined compliance verification, reduced operational costs, and ensured consistent access to accurate technical standards.

The scalable architecture allows the system to grow with the company’s data needs and evolving EMC regulations, providing a strong foundation for long-term competitiveness. By partnering with Syntronic, the automotive company gained access to specialized AI expertise that bridged the gap between legacy data challenges and the demands of next-generation automotive technology.