Abstract: Systems and methods for federated synthetic data generation are disclosed herein. The system can obtain update parameter sets for federated, local models that reside on respective server devices. The system can determine aggregation model weight values pertaining to respective local model types and apply the weight values to the update parameter sets, thereby configuring a global artificial intelligence model to generate synthetic data. The system can input the simulated dataset into a data characterization model to generate characterization datasets that include natural language characterizations of data associated with the different model types. The system can generate graphical representations of the characterization datasets for display on a user interface and deploy the simulated dataset to nodes to enable validation of data processing pipelines using the simulated dataset, thereby enabling efficient and accurate system validation tasks while mitigating exposure of sensitive data.

Abstract: Systems and methods for post-generation input stream modification for inferred network-based simulated data are disclosed herein. The system can receive modification requests for simulated datasets that include updated privacy parameter sets associated with entities contributing to the underlying data. The system can retrieve inferred entity-relationship networks that capture structural, semantic, and statistical properties of the simulated dataset to determine which data records are affected by privacy parameter changes. The system can generate updated subsets of data records consistent with the modified privacy parameters and selectively regenerate only affected portions of the simulated dataset while maintaining consistency with unchanged data. The system can transmit updated simulated datasets to targeted components of data transformation pipelines and maintain cryptographic activity logs on distributed ledgers to ensure immutable records of all modifications for technical standard compliance.

Abstract: The systems and methods disclosed herein can generate calibrated synthetic data with a measurable tracking difference/error. Using a dataset (e.g., actual data, data derived from actual data) including a set of attributes and/or a set of observed values of the set of attributes, the system can identify a subset of the set of attributes to be modified within the dataset. The system can generate a set of synthetic values of the identified subset of attributes using the dataset (e.g., based on an application domain of the dataset), the identified subset of attributes, and/or corresponding observed values of the identified subset of attributes. The system can generate a tracking relationship value between the set of synthetic data and the dataset for one or more benchmarks by comparing the observed values of the identified subset of attributes and the set of synthetic values of the identified subset of attributes.

Abstract: Systems and methods for generating simulated datasets based on inferred statistical relationships, structural relationships, and semantic relationships using knowledge networks are disclosed herein. The system can receive a node dataset that includes entities and associated relationships. The system can generate an inferred statistical dataset characterizing statistical metric information associated with the entities and the relationships, thereby enabling generation of an inferred entity-relationship network including latent and explicit relationships and statistical correlations. Based on the inferred entity-relationship network and associated constraints, the system can generate simulated data that is consistent with the statistical, structural, and semantic relationships of the received node dataset, while protecting the underlying data on which the synthetic data is based.

Abstract: The systems and methods disclosed herein receive a dataset including an observed set of values for a set of variables. The system can use a first set of AI models to identify a set of anomalies in the observed set of values by comparing an observed set of patterns against multiple reference patterns. The system can use a second set of AI models to evaluate the identified anomalies by comparing an observed set of association rules with an expected set of association rules. The system can use a third set of AI models to generate reconfiguration commands to remove the identified anomalies. The reconfiguration commands can be automatically executed to modify the observed association rules to align with the expected association rules.

Abstract: The systems and methods disclosed herein obtain (e.g., via a user interface) a collection of unstructured data, where each document includes a content set. Using a first AI model set, multiple summaries are generated by categorizing each document into clusters based on vector comparisons of content sets and summarizing the content for each cluster. A second AI model set (same as or different from the first AI model set) identifies duplicate content within the unstructured data by generating similarity values between pairs of summaries and determining if the similarity values meet a predefined threshold. A report is generated (e.g., on the user interface) indicating the duplicate content sets and/or the collection of unstructured data.

Abstract: The systems and methods disclosed herein enable generation of domain-specific data using generative artificial intelligence models by leveraging domain-specific ontology maps and lexical data. For example, the system can obtain an input prompt, a domain-specific key-value dataset and a domain-specific ontology map. The system can input the input prompt, key-value dataset, and ontology map into a domain-sensitive artificial intelligence (AI) model to generate an output that can be validated for adherence to domain-specified constraints and/or for domain-specificity by comparison with a generalized output by a non-specialized base model. Based on the validation, the system can transmit the generated output to a suitable device for data dissemination, use, or validation.

Abstract: The systems and methods disclosed herein obtain (e.g., via a user interface) a collection of unstructured data, where each document includes a content set. Using a first AI model set, multiple summaries are generated by categorizing each document into clusters based on vector comparisons of content sets and summarizing the content for each cluster. A second AI model set (same as or different from the first AI model set) identifies duplicate content within the unstructured data by generating similarity values between pairs of summaries and determining if the similarity values meet a predefined threshold. A report is generated (e.g., on the user interface) indicating the duplicate content sets and/or the collection of unstructured data.

Abstract: The systems and methods disclosed herein generate context-aware responses using semantically chunked information. The systems and methods disclosed herein partition a set of artifacts responsive to a query (e.g., a prompt for an artificial intelligence model such as a large language model) into a set of continuous chunks and associate each continuous chunk with a knowledge graph. The knowledge graph includes nodes representing chunks and edges indicating common attributes. The systems and methods disclosed herein modify node(s) in the graph by determining values of feature variables and adjusting edges in accordance with the values and generate contextualized chunks by associating or linking continuous chunks of node pairs using shared edges. The systems and methods disclosed herein use the contextualized chunks and query to generate a response using the artificial intelligence model.

Abstract: Systems and methods are disclosed comprising instructions to receive a request to evaluate authorization of a development service that comprises a digital artifact set, each digital artifact in the digital artifact set, access an authorization schema set available for the development service, identify an applicable authorization schema from the authorization schema set via comparing the content embeddings of the digital artifacts and the reference embeddings of the authorization schemas, retrieve a historical artifact attribute set representing tracked development actions for prior development services authorized via the applicable authorization schema, predict an authorization status for the development service using the historical artifact attribute set and the artifact attribute set, configure for display a visual representation of the applicable authorization schema and the mapped at least one digital artifact of the development service.

Abstract: The systems and methods disclosed herein obtain (e.g., via a user interface) a collection of unstructured data, where each document includes a content set. Using a first AI model set, multiple summaries are generated by categorizing each document into clusters based on vector comparisons of content sets and summarizing the content for each cluster. A second AI model set (same as or different from the first AI model set) identifies duplicate content within the unstructured data by generating similarity values between pairs of summaries and determining if the similarity values meet a predefined threshold. A report is generated (e.g., on the user interface) indicating the duplicate content sets and/or the collection of unstructured data.

Abstract: The systems and methods disclosed herein receive a dataset including an observed set of values for a set of variables. The system can use a first set of AI models to identify a set of anomalies in the observed set of values by comparing an observed set of patterns against multiple reference patterns. The system can use a second set of AI models to evaluate the identified anomalies by comparing an observed set of association rules with an expected set of association rules. The system can use a third set of AI models to generate reconfiguration commands to remove the identified anomalies. The reconfiguration commands can be automatically executed to modify the observed association rules to align with the expected association rules.

Abstract: Systems and methods are disclosed comprising instructions to receive a request to evaluate authorization of a development service that comprises a digital artifact set, each digital artifact in the digital artifact set, access an authorization schema set available for the development service, identify an applicable authorization schema from the authorization schema set via comparing the content embeddings of the digital artifacts and the reference embeddings of the authorization schemas, retrieve a historical artifact attribute set representing tracked development actions for prior development services authorized via the applicable authorization schema, predict an authorization status for the development service using the historical artifact attribute set and the artifact attribute set, configure for display a visual representation of the applicable authorization schema and the mapped at least one digital artifact of the development service.

Abstract: The systems and methods disclosed herein obtain (e.g., via a user interface) a collection of unstructured data, where each document includes a content set. Using a first AI model set, multiple summaries are generated by categorizing each document into clusters based on vector comparisons of content sets and summarizing the content for each cluster. A second AI model set (same as or different from the first AI model set) identifies duplicate content within the unstructured data by generating similarity values between pairs of summaries and determining if the similarity values meet a predefined threshold. A report is generated (e.g., on the user interface) indicating the duplicate content sets and/or the collection of unstructured data.

Abstract: Systems and methods are disclosed comprising instructions to receive a request to evaluate authorization of a development service that comprises a digital artifact set, each digital artifact in the digital artifact set, access an authorization schema set available for the development service, identify an applicable authorization schema from the authorization schema set via comparing the content embeddings of the digital artifacts and the reference embeddings of the authorization schemas, retrieve a historical artifact attribute set representing tracked development actions for prior development services authorized via the applicable authorization schema, predict an authorization status for the development service using the historical artifact attribute set and the artifact attribute set, configure for display a visual representation of the applicable authorization schema and the mapped at least one digital artifact of the development service.

Abstract: The systems and methods disclosed herein receive a dataset including an observed set of values for a set of variables. The system can use a first set of AI models to identify a set of anomalies in the observed set of values by comparing an observed set of patterns against multiple reference patterns. The system can use a second set of AI models to evaluate the identified anomalies by comparing an observed set of association rules with an expected set of association rules. The system can use a third set of AI models to generate reconfiguration commands to remove the identified anomalies. The reconfiguration commands can be automatically executed to modify the observed association rules to align with the expected association rules.

Abstract: The systems and methods disclosed herein generates responses generated by artificial intelligence (AI) models such as large language models (LLM) using intent-based rankings of retrieved information. The systems and methods disclosed herein receives an output generation request for the generation of an output using a set of AI models. Using a first AI model, a set of documents are retrieved using the received output generation request. The set of documents are partitioned into chunks. The chunks are ranked using a distance between the vector representation of the received output generation request and the vector representation of each chunk. A second AI model classifies the output generation request and chunks using an intent of the respective output generation request or chunk, and generates a second set of rankings using the intents. The set of AI models generate a response using the second set of rankings.

Abstract: The systems and methods disclosed herein generate context-aware responses using semantically chunked information. The systems and methods disclosed herein partition a set of artifacts responsive to a query (e.g., a prompt for an artificial intelligence model such as a large language model) into a set of continuous chunks and associate each continuous chunk with a knowledge graph. The knowledge graph includes nodes representing chunks and edges indicating common attributes. The systems and methods disclosed herein modify node(s) in the graph by determining values of feature variables and adjusting edges in accordance with the values and generate contextualized chunks by associating or linking continuous chunks of node pairs using shared edges. The systems and methods disclosed herein use the contextualized chunks and query to generate a response using the artificial intelligence model.

Abstract: The systems and methods disclosed herein generates responses generated by artificial intelligence (AI) models such as large language models (LLM) using intent-based rankings of retrieved information. The systems and methods disclosed herein receives an output generation request for the generation of an output using a set of AI models. Using a first AI model, a set of documents are retrieved using the received output generation request. The set of documents are partitioned into chunks. The chunks are ranked using a distance between the vector representation of the received output generation request and the vector representation of each chunk. A second AI model classifies the output generation request and chunks using an intent of the respective output generation request or chunk, and generates a second set of rankings using the intents. The set of AI models generate a response using the second set of rankings.