7. A system comprising: at least one computing device configured to update an expert corpus set, by performing actions including: obtaining a query from a first user; obtaining a first raw data source; determining a first relevance score for the first raw data source with respect to the query, by performing actions including: creating a first vector of statistical variables for the query using at least one natural language processing (NLP) socket, the statistical variables of the first vector having category types; creating a second vector of statistical variables for the first raw data source, the statistical variables for the first raw data source having category types that are the same as the category types of the statistical variables for the query; generating a hypothesis regarding the relevance of the first raw data source with respect to the query; testing the hypothesis by comparing each statistical variable for the query to each same statistical variable for the first raw data source; and calculating a gradient between the first vector and the second vector to determine the first relevance score; updating the expert corpus set by ingesting the first raw data source into the expert corpus in response to determining the first relevance score exceeding a first thresholds; accessing the updated expert corpus; determining at least one candidate answer to the query using an NLP algorithm; calculating a veracity score for each of the at least one candidate answers based on the NLP algorithm; and responding to the first user with at least one first candidate answer based on the veracity score.
7. A system comprising: at least one computing device configured to update an expert corpus set, by performing actions including: obtaining a query from a first user; obtaining a first raw data source; determining a first relevance score for the first raw data source with respect to the query, by performing actions including: creating a first vector of statistical variables for the query using at least one natural language processing (NLP) socket, the statistical variables of the first vector having category types; creating a second vector of statistical variables for the first raw data source, the statistical variables for the first raw data source having category types that are the same as the category types of the statistical variables for the query; generating a hypothesis regarding the relevance of the first raw data source with respect to the query; testing the hypothesis by comparing each statistical variable for the query to each same statistical variable for the first raw data source; and calculating a gradient between the first vector and the second vector to determine the first relevance score; updating the expert corpus set by ingesting the first raw data source into the expert corpus in response to determining the first relevance score exceeding a first thresholds; accessing the updated expert corpus; determining at least one candidate answer to the query using an NLP algorithm; calculating a veracity score for each of the at least one candidate answers based on the NLP algorithm; and responding to the first user with at least one first candidate answer based on the veracity score. 9. The system of claim 7 , further comprising: receiving information about the first user, wherein the determining of the first relevance score for the first raw data source with respect to the query includes: determining the statistical variables for the query based on the information about the user; and creating the first vector of statistical variables for the query.