Abstract: Methods and systems are presented for providing a knowledge bot configurable to interact with users across multiple domains. The knowledge bot includes at least a text-based search engine and a semantic-based search engine. Each of the search engine is configured to retrieve documents from a corpus of documents based on the user query. The user query is in a natural language format. The retrieved documents may be ranked according to how relevant the documents are to the user query. A subset of the documents is used as the search results based on the ranking. The search results from the search engine are combined with the user query to generate a prompt for an artificial intelligence model. Based on the prompt, a response in the natural language format is generated by the artificial intelligence model.

Abstract: Methods and systems are presented for providing a knowledge bot configurable to interact with users across multiple domains. The knowledge bot includes at least a text-based search engine and a semantic-based search engine. Each of the search engine is configured to retrieve documents from a corpus of documents based on the user query. The user query is in a natural language format. The retrieved documents may be ranked according to how relevant the documents are to the user query. A subset of the documents is used as the search results based on the ranking. The search results from the search engine are combined with the user query to generate a prompt for an artificial intelligence model. Based on the prompt, a response in the natural language format is generated by the artificial intelligence model.

Abstract: Via one or more electronic communication channels of an electronic platform, a request is detected from a user to interact with the electronic platform. Via a Natural Language Processing (NLP) model, an intent of the user behind the request to interact with the electronic platform is predicted. Via an Explainable Artificial Intelligence (XAI) model, one or more features associated with the user that contributed to the predicted intent are determined. Via a Large Language Model (LLM), a personalized message is generated for the user. The personalize message refers to the intent predicted by the NLP model or the one or more features associated with the user determined by the XAI model that contributed to the predicted intent. The personalized message is provided to the user via the one or more electronic communication channels.

Abstract: Methods and systems are presented for providing a knowledge bot configurable to interact with users across multiple domains. The knowledge bot includes at least a text-based search engine and a semantic-based search engine. Each of the search engine is configured to retrieve documents from a corpus of documents based on the user query. The user query is in a natural language format. The retrieved documents may be ranked according to how relevant the documents are to the user query. A subset of the documents is used as the search results based on the ranking. The search results from the search engine are combined with the user query to generate a prompt for an artificial intelligence model. Based on the prompt, a response in the natural language format is generated by the artificial intelligence model.

Abstract: Methods and systems are presented for providing a container orchestration framework for facilitating development and deployment of software applications across different operating environments within an enterprise system. Upon receiving a service request for processing a set of data is received, the container orchestration framework determines one or more machines that store the set of data. Instead of processing the set of data remotely, the container orchestration framework deploys a container that encapsulates an application on the one or more machines. Each application instance running on the one or more machines are executed to process a corresponding subset of data stored on the machine locally. The container orchestration framework obtains the output data from executing the applications on each of the one or more machines, and present the output data as a response to the service request.

Abstract: Methods and systems are presented for providing a knowledge bot configurable to interact with users across multiple domains. The knowledge bot includes at least a text-based search engine and a semantic-based search engine. Each of the search engine is configured to retrieve documents from a corpus of documents based on the user query. The user query is in a natural language format. The retrieved documents may be ranked according to how relevant the documents are to the user query. A subset of the documents is used as the search results based on the ranking. The search results from the search engine are combined with the user query to generate a prompt for an artificial intelligence model. Based on the prompt, a response in the natural language format is generated by the artificial intelligence model.

Abstract: Methods and systems are presented for providing a knowledge bot configurable to interact with users across multiple domains. The knowledge bot includes at least a text-based search engine and a semantic-based search engine. Each of the search engine is configured to retrieve documents from a corpus of documents based on the user query. The user query is in a natural language format. The retrieved documents may be ranked according to how relevant the documents are to the user query. A subset of the documents is used as the search results based on the ranking. The search results from the search engine are combined with the user query to generate a prompt for an artificial intelligence model. Based on the prompt, a response in the natural language format is generated by the artificial intelligence model.

Abstract: Methods and systems are presented for providing a container orchestration framework for facilitating development and deployment of software applications across different operating environments within an enterprise system. Upon receiving a service request for processing a set of data is received, the container orchestration framework determines one or more machines that store the set of data. Instead of processing the set of data remotely, the container orchestration framework deploys a container that encapsulates an application on the one or more machines. Each application instance running on the one or more machines are executed to process a corresponding subset of data stored on the machine locally. The container orchestration framework obtains the output data from executing the applications on each of the one or more machines, and present the output data as a response to the service request.

Abstract: Methods and systems are presented for providing a container orchestration framework for facilitating development and deployment of software applications across different operating environments within an enterprise system. Upon receiving a service request for processing a set of data is received, the container orchestration framework determines one or more machines that store the set of data. Instead of processing the set of data remotely, the container orchestration framework deploys a container that encapsulates an application on the one or more machines. Each application instance running on the one or more machines are executed to process a corresponding subset of data stored on the machine locally. The container orchestration framework obtains the output data from executing the applications on each of the one or more machines, and present the output data as a response to the service request.

Abstract: Methods and systems are presented for providing a container orchestration framework for facilitating development and deployment of software applications across different operating environments within an enterprise system. Upon receiving a service request for processing a set of data is received, the container orchestration framework determines one or more machines that store the set of data. Instead of processing the set of data remotely, the container orchestration framework deploys a container that encapsulates an application on the one or more machines. Each application instance running on the one or more machines are executed to process a corresponding subset of data stored on the machine locally. The container orchestration framework obtains the output data from executing the applications on each of the one or more machines, and present the output data as a response to the service request.

Abstract: Methods and systems are presented for providing a container orchestration framework for facilitating development and deployment of software applications across different operating environments within an enterprise system. Upon receiving a service request for processing a set of data is received, the container orchestration framework determines one or more machines that store the set of data. Instead of processing the set of data remotely, the container orchestration framework deploys a container that encapsulates an application on the one or more machines. Each application instance running on the one or more machines are executed to process a corresponding subset of data stored on the machine locally. The container orchestration framework obtains the output data from executing the applications on each of the one or more machines, and present the output data as a response to the service request.