Integrating Knowledge Graph and Large Language Models for Defining Business Strategies

Abstract

Effective business strategy formulation requires synthesising diverse, often conflicting information sources into coherent action plans. While Large Language Models (LLMs) show potential for processing textual information at scale, their application is limited by hallucinations and a lack of grounding in proprietary data. This paper proposes a methodology that integrates a domain-specific Knowledge Graph (KG) with a GraphRAG pipeline to generate strategic briefing documents, or Primers, which provide a structured overview of a company’s competitive environment. Our approach utilizes an ontology-first framework and Cypher-based graph traversal to capture the relational nature of strategic knowledge beyond simple vector retrieval. Experimental results on a Q&A dataset demonstrate that the Vector + Cypher retrieval strategy significantly improves grounding over LLM-only baselines and outperforms naive vector retrieval in terms of completeness and usefulness. These findings suggest that the synergy of LLMs and structured KGs provides a robust foundation for automated strategic analysis in real-world business scenarios.

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