Abstract: Techniques for machine learning-based database querying are provided. A natural language request for information contained in a database is received. Based on the natural language request and a natural language meta-schema for the database, a relevant schema comprising at least one of a subset of tables from a plurality of tables of the database, a subset of columns from a plurality of columns in the plurality of tables, or a subset of values from a plurality of values in the plurality of columns is identified. A database query is generated based on prompting one or more language models (LMs) using the relevant schema and the natural language request. A result from the database is retrieved using the database query, and a natural language response to the natural language request is generated based on the result and using the one or more LMs.

Abstract: In one embodiment, a method may access, from one or more data sources associated with a user, customer data associated with a customer of the user, wherein the customer data includes qualitative and quantitative data. The method can generate a plurality of normalized key performance indicators (KPIs) using the customer data and a normalization algorithm. The method can access a customer-health score model, wherein the customer-health score model is trained to determine a customer-health score using the plurality of normalized KPIs. The method can determine, using the customer-health score model, a customer-health score for the customer using the plurality of normalized KPIs. The method can send instructions for presenting a user interface to the user, the user interface comprising information associated with the plurality of normalized KPIs and the customer-health score for the customer.

Abstract: In one embodiment, a method includes accessing customer data that includes product-usage signals, support signals, and communication signals associated with customers and products of the business entity. The method includes analyzing the customer data by applying one or moreAI models to the customer data and determining, for each of the customers and each of the products a score for each of multiple KPIs for the business entity. The method includes determining a customer-health score for each of the customers and each of the products. The KPIs are weighted in the customer-health score according to SHAP, and the customer-health score is based on benchmarks across segments determined using a clustering algorithm applied to the customer data for the KPIs. The method includes, in response to a request from a client device, communicating the customer health-scores, the segments, and the benchmarks to the client device for display to a user.