End-to-end systems and methods for construct scoring

A system, comprising:

• a data layer configured to:
• use large language models (LLMs) to extract information identifying one or more constructors, one or more constructs, and one or more categories of constructs, wherein individual constructs of the one or more constructs are:
• associated with at least one particular constructor of the one or more constructors;
• and
• associated with at least one particular category of the one or more categories of constructs;
• store a knowledge graph of the constructors, constructs, and categories in a graph database;
• store structured data and unstructured data identifying the constructors, the constructs, and the categories in a relational database;
• and
• generate and store embeddings for images, texts, and metadata in the structured data and unstructured data in an embeddings database;
• a processing layer configured to:
• use data mining pipelines to analyze the structured data and unstructured data and the embeddings to update the knowledge graph;
• use a plurality of scoring algorithms to consume the analysis of the data mining pipelines to generate scoring indicators for the constructs;
• and
• use expert rules encoded in domain-specific languages to update the scoring indicators;
• serving layer configured to further fine tune the updated scoring indicators, to periodically write the fine-tuned updated scoring indicators to blob storage, and to use application programming interfaces (APIs) to make the fine-tuned updated scoring indicators to end users, and to display the fine-tuned updated scoring indicators across end user devices;
• and
• monitoring layer configured to generate logs and metrics for performance and data quality of other layers, apply feedback loops to improve data ingestion and model weights of the other layers, and provide retraining and benchmarking of models in the other layers.