For many years companies have relied on relational databases for day-to-day operations and financial transactions. More recently companies have employed document stores for text processing and information retrieval. Competitive intelligence as a service goes beyond traditional methods by employing a graph-relational model that has the abilities of relational, graph, document, and vector database systems.

Knowledge engineering today means working with knowledge graphs. Companies, products, and consumers represent entities or nodes in a knowledge graph. Entities have relationships, which comprise links or edges in the knowledge graph.

Building a client’s knowledge graph begins with information from surveys and online sources, but it does not end there. Using methods from marketing research, economic analysis, and graph data science, we draw inferences about marketplace entities and their relationships.

With state-of-the-art algorithms for knowledge graph completion, we explore complex relationships across markets. Insights emerge from the knowledge graph and contribute to our understanding of the competitive landscape.

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