Abstract: Aspects of the subject disclosure may include, for example, obtaining alert data (comprising a plurality of alerts) for a communications network; categorizing each of the plurality of alerts with respect to a first category and a second category (the first category having a higher severity than the second category), wherein the categorizing results in categorized data (comprising a first plurality of categorized alerts that have the relatively higher severity and a second plurality of categorized alerts that have a relatively lower severity); obtaining from a database, for each of the first plurality of categorized alerts, additional information (the first plurality of categorized alerts comprising a first categorized alert having associated therewith first additional information); and inputting the first categorized alert and the first additional information to an artificial intelligence (AI) process (the AI process outputting a recommended remediation action to apply to the communications network).

Abstract: An example method includes identifying, using a first NLP agent, a database of an enterprise containing desired data, selecting, using a second NLP agent, a schema analysis tool that is believed to be suited to a type of the database, establishing, using a third NLP agent, a connection to the database, extracting, using a fourth NLP agent, schema information from the database, comparing, using a fifth NLP agent and the schema analysis tool and based on the schema information, a structure of a schema of the database to a structure of a schema of a similar database, determining, based on the comparing, a change to make to the schema of the database, sending, using a sixth NLP agent, an instruction to a network element of the enterprise to make the change to the schema of the database, and validating, using a seventh NLP agent, the change to the schema.

Abstract: An example method includes acquiring data from at least one data source of an enterprise, detecting a plurality of characteristics of the data, selecting, based on the plurality of characteristics, a partitioning technique for partitioning the data, partitioning, using the partitioning technique, the data to produce a plurality of data chunks, generating a plurality of vector embeddings of the plurality of data chunks, selecting metadata with which to augment the plurality of vector embeddings, augmenting the plurality of vector embeddings with the metadata, and applying an indexing algorithm to the plurality of vector embeddings to facilitate a storage of the plurality of vector embeddings.

Abstract: Aspects of the subject disclosure may include, for example, obtaining first configuration information associated with a communications network, wherein the first configuration information is obtained responsive to a first provisioning request; applying the first configuration information to an AI process that utilizes an AI model that had been trained using first training data, wherein the AI process determines at least one first potential adverse effect that would be caused by implementing the first configuration information, and wherein the AI process further determines a first risk level associated with the at least one first potential adverse effect; receiving from the AI process the first risk level associated with the at least one first potential adverse effect; and outputting the first risk level. Other embodiments are disclosed.

Abstract: Aspects of the subject disclosure may include, for example, generating network configuration data for a network device according to device information of a vendor; providing the network configuration data to an Artificial Intelligence (AI) analysis platform that applies an AI model to the network configuration data based at least in part on topology data resulting in a configuration analysis, wherein the configuration analysis evaluates the network configuration data and generates at least one of rating information, adjustment information or a combination thereof. The rating information can include a security rating, a risk rating and/or an efficiency rating. The adjustment information can include a recommendation for changing at least a portion of the network configuration data for the network device.

Abstract: Aspects of the subject disclosure may include, for example, obtaining a request pertaining to a communication service, processing a file associated with the request to generate asset details, obtaining data pertaining to demand for communication services within a communication network, processing at least the data to generate a forecast of demand for the communication service, and processing the asset details and the forecast of demand to allocate capacity, acquire resources, or a combination thereof, for facilitating the communication service. Other embodiments are disclosed.

Abstract: A computer-implemented method for generating interactive actionable electronic mails, is disclosed.

Abstract: Embodiments of the present disclosure include techniques for decentralizing feature toggles. In certain embodiments, feature toggles are generated for features across a plurality of software systems. The feature toggles include annotations specifying dependencies across the plurality of software systems. The annotated feature toggles are stored in a central repository accessible by the plurality of software systems. When a feature toggle is enabled, feature toggles across the systems may be retrieved from the repository and analyzed by an LLM to determine which feature toggles may need to be updated to prevent a software error.

Abstract: Aspects of the subject disclosure may include, for example, transmitting, to a server, a request to participate in FL for an AI model, where a cloud system operates a first instance of the model and the device operates a second instance of the model, receiving, from the server, information relating to training of the second instance of the model, determining that computation(s) associated with the training are to be performed by a quantum computer, causing at least a portion of a local dataset to be provided to the quantum computer to perform the computation(s), receiving, from the quantum computer, output(s) of the computation(s), providing the output(s) to the server to enable aggregation of the output(s) with output(s) of other device(s) involved in the FL, obtaining aggregated data from the server, and utilizing the aggregated data to update the second instance of the model. Other embodiments are disclosed.

Abstract: Systems and methods include acquisition of an asynchronous message from a message producer, the asynchronous message associated with a message consumer, determination that the asynchronous message matches a stored message, identification, in response to determining that the asynchronous message matches a stored message, of a stored error message associated with the stored message, and return of a return message based on the stored error message to the message producer.

Abstract: A computer-implemented method for generating interactive actionable electronic mails, is disclosed.

Abstract: Embodiments of the present disclosure include techniques for decentralizing feature toggles. In certain embodiments, feature toggles are generated for features across a plurality of software systems. The feature toggles include annotations specifying dependencies across the plurality of software systems. The annotated feature toggles are stored in a central repository accessible by the plurality of software systems. When a feature toggle is enabled, feature toggles across the systems may be retrieved from the repository and analyzed by an LLM to determine which feature toggles may need to be updated to prevent a software error.

Abstract: Automatic evaluation of recorded interactions is disclosed, including: detecting a plurality of signals in an interaction; combining at least a subset of the plurality of signals using a prescribed set of operators into a combined signal; and determining whether an event criterion has been met with respect to the interaction based at least in part on the combined signal.

Abstract: In some implementations, there is a method In some embodiments, there may be provided a method that includes extracting a system landscape from a target system being converted from using synchronous application program interface calls among a plurality of entities and a database instance to using an asynchronous event messaging among the plurality of entities and the database instance; determining, based on the extracted system landscape, clusters indicative of call patterns among the plurality of entities and the database instance; and providing, using the clusters, at least one proposal to convert the system to the asynchronous event messaging among the plurality of entities and the database instance. Related systems, methods, and articles of manufacture are also disclosed.

Abstract: Aspects of the subject disclosure may include, for example, obtaining first user-generated input, the first user-generated input indicates a request for generating an (API, identifying a first group of entities from the request, and determining whether there is a match between the first group of entities and a second group of entities stored in an API database according to API metadata. Further embodiments include identifying an API from the API database resulting in an identified API, presenting the identified API to the user, obtaining second user-generated input, and adjusting the request according to the second user-generated. Additional embodiments can include identifying a third group of entities from the adjusted request, and determining whether there is a match between the third group of entities and a fourth group of entities stored in the API database according to the API metadata. Other embodiments are disclosed.

Abstract: A machine learning (Ml)-based computing method for forecasting financial transactions is disclosed. The ML-based computing method includes receiving inputs from users; generating cash flow data including historical cash flow data and real-time cash flow data, based on the inputs received from the users; determining a growth factor in a week and month level based on the historical cash flow data and the forecast period; determining average cash flow data in a week and month level based on the historical cash flow data, the forecast period, a lookback period received from the users, and the growth factor; generating forecast cash flow data in at least one of a week, month, quarter, and year level for the forecast period using a ML model; and providing an output of the forecast cash flow data to the one or more users on a user interface associated with one or more electronic devices.

Abstract: Systems and methods include acquisition of an asynchronous message from a message producer, the asynchronous message associated with a message consumer, determination that the asynchronous message matches a stored message, identification, in response to determining that the asynchronous message matches a stored message, of a stored error message associated with the stored message, and return of a return message based on the stored error message to the message producer.

Abstract: Automatic evaluation of recorded interactions is disclosed, including: detecting a plurality of signals in an interaction; combining at least a subset of the plurality of signals using a prescribed set of operators into a combined signal; and determining whether an event criterion has been met with respect to the interaction based at least in part on the combined signal.

Abstract: Transaction records are obtained and respective volumes of the transaction records during a plurality of intervals are determined based on timestamps. A spread of a distribution of the respective volume is determined. Whether the respective volumes exceeds a predetermined volume threshold is determined. Then, an integration flow is selected from among a plurality of integration flows based on the respective volumes, the spread, and whether respective volumes exceeds the predetermined volume threshold. Services and application programming interfaces corresponding to the selected integration flow are configured. Then data is replicated from the database system to a cloud platform using the configured services and application programming interfaces. A portal application is provided to present the data on the cloud platform.

Abstract: Transaction records are obtained and respective volumes of the transaction records during a plurality of intervals are determined based on timestamps. A spread of a distribution of the respective volume is determined. Whether the respective volumes exceeds a predetermined volume threshold is determined. Then, an integration flow is selected from among a plurality of integration flows based on the respective volumes, the spread, and whether respective volumes exceeds the predetermined volume threshold. Services and application programming interfaces corresponding to the selected integration flow are configured. Then data is replicated from the database system to a cloud platform using the configured services and application programming interfaces. A portal application is provided to present the data on the cloud platform.