Abstract: Systems and methods for adapting an onboarding session to a user are disclosed. An example method is performed by one or more processors of a system and includes receiving a transmission over a communications network from a computing device associated with a user of the onboarding system, the transmission including one or more files, extracting data from each of the one or more files using one or more parser plugins, transforming the extracted data into a set of arrays, feeding a prompt including the set of arrays to a large language model (LLM), inferring characteristics of the user based on a response to the prompt from the LLM, mapping the inferred characteristics to a predefined list of system features, and optimizing components of an onboarding session for the user based on the mapping.

Abstract: A method for performing a parity check of a table by a software application may include obtaining, from a data lake, data lake records stored in the table during a time interval, obtaining partitioning information used to partition the table in a database during the time interval, extracting, from the data lake records and for the partitioning information, partition identifiers stored in the table during the time interval, generating a partition-specific database query including a partition identifier, executing the partition-specific database query to obtain database records stored in the table in a partition of the database during the time interval, extracting a subset of the data lake records that include the partition identifier, and performing a parity comparison on the subset of the data lake records and the database records to generate a parity result.

Abstract: Systems and methods for intelligently repairing data are disclosed. An example method is performed by one or more processors of a data quality management (DQM) system and includes receiving a transmission over a communications network from a computing device associated with the DQM system, the transmission including an indication that source data stored in a source database was ingested and stored as target data in a target database at a time of ingestion, comparing, using an advanced DQM algorithm, the target data with the source data, the advanced DQM algorithm including generating a first set of parity results based on changes occurring before the time of ingestion, generating a second set of parity results based on changes occurring after the time of ingestion, and generating differential results based on the first and the second set of parity results, and selectively repairing ones of the changes based on the differential results.

Abstract: Systems and methods for detecting errors in a data transfer uses a machine learning model to identify potential anomalies in the data transfer based on metadata. Mismatches between input data from the data transfer and output data after importing the data transfer may additionally be identified. User review and correction of data errors and potential anomalies identified using the machine learning model may be proactively prompted to ensure any errors or discrepancies are addressed before finalizing the import of the data transfer. User corrections are further used to retrain the machine learning model to enable continuous improvement and learning from the data transfer process.

Abstract: A method implements efficient real time serving of ensemble models. The method includes receiving an input and processing the input with an abridged model to generate a set of component scores and an abridged score. The method further includes processing the set of component scores with a deviation threshold to select one of the abridged score and an ensemble score as an output and presenting the output.

Abstract: Aspects of the present disclosure provide techniques for providing a graphical user interface for customizable application navigation. Embodiments include displaying a list of pages associated with a software application in a navigation customization screen and receiving selections of two or more pages of the pages as bookmarks. Embodiments include receiving drag and drop input via the navigation customization screen that changes an ordering of the two or more pages within the list of the plurality of pages and receiving a search query comprising a text string. Embodiments include moving one or more pages matching the search query to a top of the list of the pages within the navigation customization screen and displaying an indication in the navigation customization screen that one of the two or more pages also matches the search query without changing the ordering of the two or more pages within the list of the pages.

Abstract: A method including extracting data from disparate data sources. The data includes data pairs including a corresponding data point and a corresponding time associated with the corresponding data point. The method also includes extracting insights from the data at least by identifying a trend in the data pairs. The method also includes forming a model vector including the insights and an additional attribute to the insights. The additional attribute characterizes the insights. The additional attribute includes at least user feedback including a user ranking of a ranked subset of the insights from a user. The method also includes inputting the model vector into a trained insight machine learning model to obtain a predicted ranking of the insights. The method also includes selecting, based on the predicted user ranking, a pre-determined number of insights to form predicted relevant insights. The method also includes reporting the predicted relevant insights.

Abstract: Certain aspects of the present disclosure provide techniques for providing smart content to a user of an application. Embodiments include receiving a request from a client for content. The request may include context data. Embodiments include identifying a content template for the content based on the request. Embodiments include identifying a rule associated with the content template. Embodiments include evaluating the rule based on the context data in order to determine a value of a variable. Embodiments include generating personalized content based on the content template and the value of the variable. Embodiments include providing the personalized content to the client.

Abstract: A method includes receiving, at a server from a user device, a user query to a large language model (LLM), creating an LLM query from the user query, inserting an security marker instruction into the LLM query to trigger an injection of a security marker, and sending the LLM query to the LLM. The method further includes receiving, from the LLM, an LLM response to the LLM query, evaluating the LLM response to detect whether the security marker is present in the LLM response, and setting a prompt injection signal based on whether the security marker is present in the LLM response.

Abstract: A method includes receiving, at a server from a user device, a user query to a large language model (LLM), creating an LLM query from the user query and an application context, gathering confidential information from the LLM query, and sending the LLM query to the LLM. The method includes receiving, from the LLM, an LLM response to the LLM query, comparing the LLM response to the confidential information to generate comparison result, and setting a leakage detection signal based on comparison result.

Abstract: Systems and methods are disclosed for managing categorization problem solutions and identifying miscategorizations. The identification of a miscategorization of an object is based on the object's first embedding being different than the first embeddings of other objects in a cluster. The objects in the cluster are clustered together based on second embeddings of the objects, with the first embedding generated based on a first description associated with an object and the second embedding generated based on a second description associated with the object. As such, while the clustering of second embeddings may initially indicate that the objects in the cluster are similar, the comparison between first embeddings of the objects in the cluster (such as calculating a distance between a first embedding and a center of the cluster based on the first embeddings) can confirm whether an object in the cluster is different and thus is potentially miscategorized.

Abstract: Systems and methods are disclosed for converting natural language queries to a query instruction set for searching a data warehouse. To generate a query instruction set from a natural language query, a system iteratively uses a generative artificial intelligence (AI) model and database query tools to generate a query instruction set in a stepwise manner. The system and generative AI model do not require a priori knowledge of data table contents in the data warehouse, which may include sensitive information. In addition, the system does not require access to the data warehouse to generate the query instruction set. Instead, the system is implemented to use structure information from the data warehouse, including table lists (such as table names) and table format information (such as column names) of tables in the data warehouse, and the generative AI model is a generally trained model to generate the query instruction set.

Abstract: A method including receiving, at a large language model, a prompt injection cyberattack. The method also includes executing the large language model. The large language model takes, as input, the prompt injection cyberattack and generates a first output. The method also includes receiving, by a guardian controller, the first output of the large language model. The guardian controller includes a machine learning model and a security application. The method also includes determining a probability that the first output of the large language model is poisoned by the prompt injection cyberattack. The method also includes determining whether the probability satisfies a threshold. The method also includes enforcing, by the guardian controller and responsive to the probability satisfying the threshold, a security scheme on use of the first output of the large language model by a control application. Enforcing the security scheme mitigates the prompt injection cyberattack.

Abstract: Systems and methods are provided for using vector embeddings and large language models to answer chatbot inquiries.

Abstract: Systems and methods for determining ownership of cloud computing resources are disclosed. An example method includes identifying a first active table whose ownership is not defined in a central repository, determining, based on a write log associated with the first active table, a first timestamp and a first internet address associated with a most recent write to the first active table, determining, based on the first internet address, whether or not the first timestamp is more recent than a creation time of a first cloud computing instance corresponding to the most recent write, and in response to the first timestamp being more recent than the creation time of the first cloud computing instance, identifying a first owner of the first active table based on a first cost allocation tag associated with the first cloud computing instance.

Abstract: A method includes receiving, at a server from a user device, a user query to a large language model (LLM), creating an LLM query from the user query, inserting a system prohibited request into the LLM query to generate a revised LLM query, and sending the revised LLM query to the LLM. The method further includes receiving, from the LLM, a first LLM response to the LLM query, testing the first LLM response to detect whether a prohibited response to the system prohibited request is included in the first LLM response, and setting a prompt injection signal based on whether the prohibited response to the system prohibited request is included in the first LLM response.

Abstract: A computing system identifies an incoming voice call from a user device to an agent device associated with the computing system. The computing system generates a transcription of the incoming voice call using one or more natural language processing techniques. The computing system extracts a problem description from the transcription. The problem description indicates a topic for the incoming voice call. A first machine learning model estimates a situation vector from the problem description. A second machine learning model identifies a pre-existing situation vector that closely matches the estimated situation vector. The computing system retrieves a situation description that corresponds to the identified pre-existing situation vector.

Abstract: Systems and methods for enriching raw user text with a database to identify relevant context, wherein generated prompts provide responses to user queries is provided. A method includes receiving a query, wherein the query comprises the raw text, creating a first embedding based on the query, retrieving a plurality of other embeddings, wherein the plurality of other embeddings are complementary to the first embedding, creating a contextual prompt including context from at least one of the plurality of other embeddings, processing the contextual prompt using a trained machine learning model, thereby generating a response to the query, and causing the response to be displayed by a display device.

Abstract: A method including detecting, in a written electronic communication, an input sentence satisfying a readability metric threshold. The method also includes transforming, by a sentence transformer model, the input sentence to output suggested sentences. The method also includes evaluating the suggested sentences along a set of acceptability criteria. The method also includes determining, based on the evaluating, that the set of acceptability criteria is satisfied. The method also includes modifying, based on determining that the set of acceptability criteria is satisfied, the written electronic communication with the suggested sentences to obtain a modified written electronic communication. The method also includes returning the modified written electronic communication.