At FiscalNote, we ingest, structure, and classify tens of thousands of new data points every day. We are constantly updating millions of documents and tens of thousands of people in our database, creating so much potentially relevant information for you and your organization to track. How do we ensure that nothing falls through the cracks?
Our system incorporates an internal knowledge graph, which contains relevant data points and maps the relationships between them. We know that a single data point on its own is an incomplete picture of more complex real-world developments; to help our customers capture and better understand the importance of individual data points, we show the connections between pieces of information.
The relationships we create between data points are made possible through our unique machine learning and natural language processing systems. Our machine-enabled analyses add structure to and derive insights from complex, unstructured data. This allows us to identify patterns and improve how we gather relevant information.
By mapping every piece of legislation, federal and state regulations, legislators, staffers, regulators, and companies, we can identify and surface the right information at the right time for our customers. Using the knowledge graph, we discover new issue areas, map trends in known issues, recommend important partners and stakeholders, and help organizations collaborate across functions.
As new information comes in, it’s placed into the existing framework, which in turn grows and improves our knowledge graph. By allowing you to interact directly with our system, we gather your feedback and are able to grow with you. Not only is our system learning for itself, it’s learning to better understand what our customers consider relevant and actionable.