Automating Trademark Intelligence: An Llm-Based Multi-Agent Framework

Trademarks function as vital early-warning signals in competitive intelligence (CI), yet integrating them into strategic decision-making remains challenging due to analytical noise and the disconnect between filing data and market context. This paper introduces an LLM-driven multi-agent system designed to operationalize trademark data within AI-augmented CI workflows. The proposed architecture employs a seven-agent pipeline to orchestrate problem formulation, evidence acquisition, and strategic synthesis, linking structured trademark records to unstructured web evidence. The system’s performance was evaluated across five diverse market scenarios using five distinct reasoning models (Mistral, Anthropic, Grok, OpenAI, and Gemini). Evaluation utilized an incremental metric framework assessing Source Grounding, Citation Quality, and Actionability. Results of this pilot study indicate that while Grok demonstrated superior retrieval grounding, Mistral emerged as the most effective model for decision support (Overall Score: 0.733), distinguishing itself through highly structured, action-able recommendations. The findings suggest that while multi-agent systems significantly enhance the efficiency of identifying weak market signals, human expertise re-mains essential for validating strategic implications.