All databases are different: the data they hold and the structure of the database itself can vary widely from organization to organization. An AI-driven database access solution can’t be one-size-fits-all because by their very nature, each unique industry (and even each individual business) necessitates unique database requirements.

Enterprise-grade databases are particularly complex and hold plenty of unique data. Because each database has distinct structure, schema, and business logic, the training corpus that is required to train the AI system for each given database must also be unique to that database. Thanks to the automated corpus generation process we’ve developed, it’s possible to rapidly build out a robust training dataset and, from that, efficiently and optimally train a custom language model specific to a given database.

Similar to the necessary task of generating custom training data, building a custom language model for a specific database allows the AI to understand both the nuances of that database’s architecture and the nuances of human language surrounding that database, inclusive of the specifics of the associated business domain.

Databases store unique data such as customer names, vendor names, and item names. A conversational AI system therefore needs to be able to understand, for example, that “Amazon” may be a customer, while in a different context in that same database, “Amazon” may also be a vendor. With great training data and a robust language model that is equipped with powerful disambiguation capabilities, the AI is able to successfully differentiate between these kinds of data points and return the appropriate data to the querent.

In the following section, we’ll dive a little deeper into how this kind of system holistically handles the issue of complexity in natural language in order to continually optimize user experience.