Abstract: A system and method for facilitating predictive maintenance of an equipment is disclosed. The system may include a data capturer, a plurality of edge computing nodes and a cloud computing device. Each edge computing node may include a first processor. The cloud computing device may include a second processor. The first processor may receive the raw input data from the data capturer and may process the raw input data to obtain a representative data. The representative data may include an insight pertaining to a deviation in the at least one variable and a corresponding remedial action to be taken to correct the deviation. The deviation may be related to a deterioration in the condition of the equipment. The respective edge computing node may facilitate a regulation of the deviation by performing an automated actuation based on the corresponding remedial action.

Abstract: A system and method for facilitating predictive maintenance of an equipment is disclosed. The system may include a data capturer, a plurality of edge computing nodes and a cloud computing device. Each edge computing node may include a first processor. The cloud computing device may include a second processor. The first processor may receive the raw input data from the data capturer and may process the raw input data to obtain a representative data. The representative data may include an insight pertaining to a deviation in the at least one variable and a corresponding remedial action to be taken to correct the deviation. The deviation may be related to a deterioration in the condition of the equipment. The respective edge computing node may facilitate a regulation of the deviation by performing an automated actuation based on the corresponding remedial action.

Abstract: Examples for a prospect assessment system are provided. The system may receive a query from a user and obtain prospect data from a plurality of data sources. The system may create a plurality of prospect clusters. The system may determine a cluster significance value. The system may determine a prospect churn value. The system may determine a set of significant prospect clusters from among the plurality of prospect clusters having the cluster significance value and the prospect churn value above a threshold value. The system may determine a prospect pattern matrix including a plurality of patterns from the set of significant prospect clusters. The system may identify a set of products for each of the plurality of patterns. The system may determine a prospect profile for each of the plurality of patterns. The system may generate a prospect assessment result based on the prospect profile for the resolution of the query.

Abstract: Examples of prospect recommendation including a prospect recommendation system are provided. The system may receive a prospect assessment query. The system may receive and sort prospect data, sales attribute data, and product data by applying an artificial intelligence component. The system may determine a prospect matrix by correlating the prospect data with the product data. The system may determine a prospect profile by collating historical prospect data and the product data. The system may determine a prospect assessment matrix by correlating the prospect matrix and the prospect profile. The system may determine a decisional pathway based on a comparison between the prospect profile and the prospect assessment matrix. The decisional pathway may include a plurality of assessment interpretations. The system may determine a prospect recommendation index based on the decisional pathway.

Abstract: Examples for a prospect assessment system are provided. The system may receive a query from a user and obtain prospect data from a plurality of data sources. The system may create a plurality of prospect clusters. The system may determine a cluster significance value. The system may determine a prospect churn value. The system may determine a set of significant prospect clusters from among the plurality of prospect clusters having the cluster significance value and the prospect churn value above a threshold value. The system may determine a prospect pattern matrix including a plurality of patterns from the set of significant prospect clusters. The system may identify a set of products for each of the plurality of patterns. The system may determine a prospect profile for each of the plurality of patterns. The system may generate a prospect assessment result based on the prospect profile for the resolution of the query.

Abstract: Examples of prospect recommendation including a prospect recommendation system are provided. The system may receive a prospect assessment query. The system may receive and sort prospect data, sales attribute data, and product data by applying an artificial intelligence component. The system may determine a prospect matrix by correlating the prospect data with the product data. The system may determine a prospect profile by collating historical prospect data and the product data. The system may determine a prospect assessment matrix by correlating the prospect matrix and the prospect profile. The system may determine a decisional pathway based on a comparison between the prospect profile and the prospect assessment matrix. The decisional pathway may include a plurality of assessment interpretations. The system may determine a prospect recommendation index based on the decisional pathway.

Abstract: A device may process application data or historical data to identify a set of metrics to be used to analyze a set of applications or to identify baseline values for the set of metrics. The device may determine a score, for each application of the set of applications, based on values for the set of metrics for the each application of the set of applications. The device may determine a refined classification for the each application of the set of applications based on the historical data or information related to the classification or the score. The device may generate a set of recommendations related to optimizing a current deployment of the set of applications. The device may perform an action to implement the set of recommendations related to optimizing the current deployment of the set of applications.

Abstract: A device may process application data or historical data to identify a set of metrics to be used to analyze a set of applications or to identify baseline values for the set of metrics. The device may determine a score, for each application of the set of applications, based on values for the set of metrics for the each application of the set of applications. The device may determine a refined classification for the each application of the set of applications based on the historical data or information related to the classification or the score. The device may generate a set of recommendations related to optimizing a current deployment of the set of applications. The device may perform an action to implement the set of recommendations related to optimizing the current deployment of the set of applications.