Abstract: Generating profiles that consolidate tenant data with interaction and transaction records from heterogeneous data sources in real time. Executing AI agents including an orchestration agent that orchestrates other agents. Providing a model interface layer that securely connects to an external machine learning model of a third-party agent while enforcing data governance rules, wherein the machine learning model can securely access customer data from the profiles of the multi-tenant platform under context-aware policies. Providing secure access to data from the profiles to the model through the model interface layer without exporting the data into a separate repository, such that the model processes live enterprise data in place. Receiving a predictive output derived from the exported data. Updating a profile by writing the predictive output as a new attribute of that profile, thereby enriching the profile with machine-generated insights in real time to create an enriched profile.

Abstract: Profiling, by a data profiler agent, an external dataset intended for integration into a multi-tenant platform. Determining data quality metrics for the dataset. Identifying data quality issues in the dataset that are predicted to adversely impact entity resolution operations of the multi-tenant platform. Identifying at least a portion of the dataset that is predicted not to adversely impact entity resolution operations of the multi-tenant platform. Generating recommended data improvement actions, wherein the data improvement actions include corrections or enrichments for the dataset. Generating and storing data profiling results. Ingesting the portion of the dataset that is predicted not to adversely impact the entity resolution operations of the multi-tenant platform into the multi-tenant platform. Executing a first data improvement action on the portion of the dataset associated with the first data improvement action, thereby generating a first improved portion of the dataset.

Abstract: Among other techniques, techniques for providing feedback associated with personally identifiable information (PII) to a customer that is a member of an industry are described. The feedback includes a comparison with dynamic industry state. A system utilizing these techniques can include a continuous initially personally identifiable information (IPII) destruction engine; an IPII feature extraction engine coupled to the continuous IPII destruction engine; a bit labeling engine coupled to the continuous IPII destruction engine; a classified and interpreted IPII attribute instance bits datastore coupled to the bit labeling engine; and an auto-meta comparator feedback engine coupled to the classified and interpreted IPII attribute instance bits datastore.

Abstract: Ingesting data from a plurality of data sources. Converting the data into a plurality of vectors, wherein each vector represents a respective object of the data. Comparing the plurality of vectors with each other. Determining a distance between the plurality of vectors based on the comparison. Receiving a query. Automatically determining a set of candidate matches based on the query and the determined distances between the plurality of vectors based on the comparison. Resolving the query based on matching one or more portions of the query with the set of candidate matches.

Abstract: Among other techniques, techniques for machine learning-based entity resolution are described.

Abstract: Among other techniques, techniques for providing feedback associated with personally identifiable information (PII) to a customer that is a member of an industry are described. The feedback includes a comparison with dynamic industry state. A system utilizing these techniques can include a continuous initially personally identifiable information (IPII) destruction engine; an IPII feature extraction engine coupled to the continuous IPII destruction engine; a bit labeling engine coupled to the continuous IPII destruction engine; a classified and interpreted IPII attribute instance bits datastore coupled to the bit labeling engine; and an auto-meta comparator feedback engine coupled to the classified and interpreted IPII attribute instance bits datastore.

Abstract: Among other techniques, techniques for machine learning-based entity resolution are described.

Abstract: Among other techniques, techniques for providing feedback associated with personally identifiable information (PII) to a customer that is a member of an industry are described. The feedback includes a comparison with dynamic industry state. A system utilizing these techniques can include a continuous initially personally identifiable information (IPII) destruction engine; an IPII feature extraction engine coupled to the continuous IPII destruction engine; a bit labeling engine coupled to the continuous IPII destruction engine; a classified and interpreted IPII attribute instance bits datastore coupled to the bit labeling engine; and an auto-meta comparator feedback engine coupled to the classified and interpreted IPII attribute instance bits datastore.