Autonomous multifactor generative artificial intelligence framework

A system comprising one or more processors and at least one non-transitory memory comprising instructions that, with the one or more processors, cause the system to:

• detect a process trigger that identifies a target entity;
• identify, entity data associated with the target entity;
• generate, using a machine learning based prediction model, a predictive asset output based at least in part on the entity data, wherein the predictive asset output comprises at least one predicted asset;
• generate, using a dynamic contextualization model, a multifactor contextualized asset representation for the at least one predicted asset based at least in part on the at least one predicted asset, the entity data, and communication channel data, wherein the multifactor contextualized asset representation comprises natural language generative output that integrates one or more datum representative of the at least one predicted asset with one or more datum representative of contextually relevant asset features selected from asset features associated with the at least one predicted asset;
• and
• transmit the multifactor contextualized asset representation to one or more computing devices via one or more communication channels.

One or more non-transitory computer-readable media storing processor-executable instructions that, when executed by one or more processors, cause the one or more processors to perform operations comprising:

• detecting a process trigger that identifies a target entity;
• identifying entity data associated with the target entity;
• generating, using a machine learning based prediction model, a predictive asset output based at least in part on the entity data, wherein the predictive asset output comprises at least one predicted asset;
• generating, using a dynamic contextualization model, a multifactor contextualized asset representation for the at least one predicted asset based at least in part on the at least one predicted asset, the entity data, and communication channel data, wherein the multifactor contextualized asset representation comprises natural language generative output that integrates one or more datum representative of the at least one predicted asset with one or more datum representative of contextually relevant asset features selected from asset features associated with the at least one predicted asset;
• and
• transmitting the multifactor contextualized asset representation to one or more computing devices via one or more communication channels.