Machine learning framework that extracts actionable insights from disparate data sources

A system comprising:

• a server comprising a processor;
• a data repository in communication with the processor, and configured to store:
• diverse data comprising first data from a first source and second data from a second source, the second source being disparate from the first source, the first data having a first attribute, and the second data having a second attribute,
• compressed data in a low-dimensional latent space, and
• a merged feature set for the low-dimensional latent space,
• a large language mode (LLM), wherein the processor is programmed to apply the LLM to natural language text and a user query to generate an actionable insight;
• and
• a server controller executable by the processor to perform operations comprising:
• collecting the diverse data to generate aggregated data in a unified dataset,
• performing an autoencoding of the aggregated data to generate the compressed data,
• merging the first attribute and the second attribute to obtain the merged feature set,
• selecting a response vector from the merged feature set in the low-dimensional latent space that is aligned with the user query, wherein the merged feature set is generated by:
• collecting the diverse data to generate the aggregated data in the unified dataset,
• performing the autoencoding of the aggregated data to generate the compressed data in the low-dimensional latent space, wherein the compressed data are representations of the aggregated data in the low-dimensional latent space, and
• merging the first attribute and the second attribute to obtain the merged feature set for the low-dimensional latent space,
• decoding the response vector into the natural language text,
• executing the LLM on the natural language text and the user query to generate the actionable insight, and
• presenting the actionable insight as a response to the user query.

A non-transitory computer readable storage medium storing computer readable program code which, when executed by at least one processor, cause the at least one processor to perform operations comprising:

• selecting a response vector from a merged feature set in a low-dimensional latent space that is aligned with a user query, wherein the merged feature set is generated by:
• collecting diverse data to generate aggregated data in a unified dataset, wherein the diverse data comprises first data from a first source and second data from a second source, the second source being disparate from the first source, the first data having a first attribute, and the second data having a second attribute,
• performing an autoencoding of the aggregated data to generate compressed data in the low-dimensional latent space, wherein the compressed data are representations of the aggregated data in the low-dimensional latent space, and
• merging the first attribute and the second attribute to obtain the merged feature set for the low-dimensional latent space;
• decoding the response vector into natural language text;
• executing a large language mode (LLM) to the natural language text and the user query to generate an actionable insight;
• and
• presenting the actionable insight as a response to the user query.