Apparatus and a method for the generation and improvement of procedure data

1 . An apparatus for generation and improvement of procedure data, wherein the apparatus comprises:

at least a processor; and

a memory communicatively connected to the at least a processor, wherein the memory contains instructions configuring the at least a processor to:

receive an entity profile from an entity, wherein the entity profile comprises a plurality of procedure data, wherein the plurality of procedure data comprises data associated with at least one or more internal and external procedures as they relate to the entity;

determine an entity cohort as a function of identified keywords associated with the operational capability, wherein the entity cohort comprises a collection of entities that are similar to a current entity;

determine capability training data based on a selection of the entity cohort associated with the plurality of procedure data and the entity profile of the current entity;

train a capability machine-learning model using the determined capability training data;

identify, by the capability machine-learning model, an operational capability associated with the entity profile as a function of the plurality of procedure data, wherein the operational capability comprises at least a resource necessary to carry out an operational objective;

determine demand data as a function of the operational capability, wherein the demand data comprises a demand scope identifying segments of a market based on a market demand related to the plurality of procedure data, wherein determining the demand data comprises:

receiving demand training data comprising a plurality of operational capabilities correlated to examples of demand data;

iteratively updating the demand training data with past outputs of demand data tailored to operational capabilities of entities;

sanitizing the updated demand training data to remove at least one input and/or output data entry within the updated demand training data,

wherein the removed at least one input and/or output data entry comprises an outlier data entry;

training a demand machine-learning model with the sanitized updated demand training data; and

outputting, by the demand machine-learning model, the demand data;

generate a demand score as a function of the demand data;

plot a plurality of graphical data as a function of the demand score, wherein plotting the plurality of graphical data further comprises:

generating the graphical data using a graphical machine-learning model; and

plotting the generated graphical data;

identify a plurality of demand clusters as a function of the plurality of graphical data, wherein identifying the plurality of demand clusters further comprises clustering the plurality of demand clusters based on a distribution of data points within each demand cluster of the plurality of demand clusters as a function of a distance of the data points from a center of the distribution;

generate modification data as a function of the plurality of demand clusters, wherein the modification data comprises an automation suggestion and wherein the automation suggestion comprises information indicating one or more portions of the plurality of procedure data would be more in demand if the one or more portions of the plurality of procedure data used automated technology and wherein the automation suggestion further comprises information associated with an implementation of a hardware by the entity to address the one or more portions of the plurality of procedure data; and

display the modification data using a display device.

2 . The apparatus of claim 1 , wherein the demand data comprises one or more free zones.

3 . The apparatus of claim 2 , wherein the one or more free zones comprises one or more target groups.

4 . The apparatus of claim 1 , wherein receiving the entity profile from the entity comprises receiving the entity profile from a web crawler, wherein the web crawler is provided with a seed set comprised of at least a website associated with the entity.

5 . The apparatus of claim 1 , wherein receiving the entity profile from the entity comprises receiving the entity profile from a chatbot, wherein the chatbot is configured to generate the entity profile using a chatbot input.

6 . The apparatus of claim 1 , wherein generating the modification data comprises generating the modification data using a modification machine learning model.

7 . The apparatus of claim 6 , wherein generating the modification data using the modification machine learning model comprises:

training the modification machine learning model using modification training data, wherein the modification training data contains a plurality of data entries containing the plurality of demand clusters as an input correlated to the modification data as an output; and

generate the modification data as a function of the plurality of demand clusters using a trained modification machine learning model.

8 . The apparatus of claim 1 , wherein generating the modification data comprises generating the modification data using a fuzzy inference set.

9 . A method for generation and improvement of procedure data, wherein the method comprises:

receiving, using at least a processor, an entity profile from an entity, wherein the entity profile comprises a plurality of procedure data, wherein the plurality of procedure data comprises data associated with at least one or more internal and external procedures as they relate to the entity;

determining an entity cohort as a function of identified keywords associated with the operational capability, wherein the entity cohort comprises a collection of entities that are similar to a current entity;

determining capability training data based on a selection of the entity cohort associated with the plurality of procedure data and the entity profile of the current entity;

training a capability machine-learning model using the determined capability training data;

identifying, by the capability machine-learning model, using the at least a processor, an operational capability associated with the entity profile as a function of the plurality of procedure data;

determining, using the at least a processor, demand data as a function of the operational capability, wherein the demand data comprises a demand scope identifying segments of a market based on a market demand related to the plurality of procedure data, wherein the operational capability comprises at least a resource necessary to carry out an operational objective, wherein determining the demand data comprises:

receiving demand training data comprising a plurality of operational capabilities correlated to examples of demand data;

iteratively updating the demand training data with past outputs of demand data tailored to operational capabilities of entities;

sanitizing the updated demand training data to remove at least one input and/or output data entry within the updated demand training data, wherein the removed at least one input and/or output data entry comprises an outlier data entry;

training a demand machine-learning model with the sanitized updated demand training data; and

outputting, by the demand machine-learning model, the demand data;

generating, using the at least a processor, a demand score as a function of the demand data;

plotting, using the at least a processor, a plurality of graphical data as a function of the demand score, wherein plotting the plurality of graphical data further comprises:

generating the graphical data using a graphical machine-learning model; and

plotting the generated graphical data;

identifying, using the at least a processor, a plurality of demand clusters as a function of the plurality of graphical data, wherein identifying the plurality of demand clusters further comprises clustering the plurality of demand clusters based on a distribution of data points within each demand cluster of the plurality of demand clusters as a function of a distance of the data points from a center of the distribution;

generating, using the at least a processor, modification data as a function of the plurality of demand clusters, wherein the modification data comprises an automation suggestion and wherein the automation suggestion comprises information indicating one or more portions of the plurality of procedure data would be more in demand if the one or more portions of the plurality of procedure data used automated technology and wherein the automation suggestion further comprises information associated with an implementation of a hardware by the entity to address the one or more portions of the plurality of procedure data; and

displaying the modification data using a display device.

10 . The method of claim 9 , wherein the demand data comprises one or more free zones.

11 . The method of claim 10 , wherein the one or more free zones comprises one or more target groups.

12 . The method of claim 9 , wherein method further comprises receiving, using the at least a processor, the entity profile from a web crawler, wherein the web crawler is provided with a seed set comprised of at least a website associated with the entity.

13 . The method of claim 9 , wherein method further comprises receiving, using the at least a processor, the entity profile from a chatbot, wherein the chatbot is configured to generate the entity profile using a chatbot input.

14 . The method of claim 9 , wherein the method further comprises generating, using the at least a processor, the modification data using a modification machine learning model.

15 . The method of claim 14 , wherein generating the modification data using the modification machine learning model comprises:

training the modification machine learning model using modification training data, wherein the modification training data contains a plurality of data entries containing the plurality of demand clusters as an input correlated to the modification data as an output; and

generate the modification data as a function of the plurality of demand clusters using a trained modification machine learning model.

16 . The method of claim 9 , wherein the method further comprises generating, using the at least a processor, the modification data using a fuzzy inference set.