Thomson Reuters, a group working with state, federal, and local governments, is indexing its information to enable analysts and subject matter experts to easily search for insights. The company faces challenges in putting data into the hands of these experts due to data silos and the need for contextual or predictive search capabilities. To address this, Thomson Reuters uses knowledge graphs, which are a key component of its architecture. The graph is built using multiple stages, including mapping, stitching, tagging, and indexing, with Neo4j playing a crucial role in the indexing phase. The company's goal is to provide a searchable index that enables subject matter experts to gain insights from the data. Thomson Reuters has applied this approach to various use cases, such as localized centrality, relationship validation, and investment trends analysis. The company invites others to join graph conversations and provides resources for entity resolution, open identifiers, and graph literacy.