Synaptiq conducted a two-stage, proof-of-concept project that transformed a large volume of our client’s unstructured documents into a queryable knowledge base. In the first stage, we used a branch of AI called natural language processing to extract units of knowledge, called "entities," from our client’s documents. Our client’s domain experts labeled a small portion of these entities, which we used to train classifiers to label the remaining entities.

In the second stage, we developed statistical models to find connections between entities. Using these models, we built a "knowledge graph," which is a database of knowledge organized according to the connections between entities in a web-like graph structure.

Our client's employees could then use this knowledge graph to search for specific knowledge related to problems, industries, or other categories of information.