Zhang, Xiaoke, Angela Kwon, Mi Zhou, Gene Moo Lee “Designing for Designers: A Multi-Agent Multi-Representational AI System to Enhance Automotive Design,” Work-in-progress.
- Industry partner: Hyundai Kia Motor’s Digital Design team
- Presentations: INFORMS (2025), KIA (2026)
Organizations increasingly seek to use large language models (LLMs) to support knowledge-intensive work. However, effective deployment requires systems that ground LLM reasoning in heterogeneous, domain-specific knowledge. In collaboration with the vehicle design team of a major automotive manufacturer, we develop Design Insight Atlas, a multi-agent, multi-representational retrieval-augmented generation (RAG) AI system for automotive design intelligence. The system grounds LLM responses in three complementary knowledge representations: structured vehicle specifications for factual analysis, automotive news for temporal and market intelligence, and a knowledge graph for relational reasoning. A central designer assistant orchestrates a news retrieval tool, a data analysis agent, and a knowledge graph analysis agent, integrating their outputs into unified, evidence-grounded responses. We evaluate the system using 160 designer-oriented questions across eight task categories and four backbone LLMs. Design Insight Atlas achieves an average overall win rate of 89.2% against vanilla LLM baselines and consistently improves comprehensiveness, accuracy, and actionability across models and question types. Our study demonstrates how multi-representational knowledge grounding and multi-agent retrieval can enhance LLM support for domain-specific organizational knowledge work.