Remediation of unstructured data using artificial intelligence

1 . A system comprising:

at least one hardware processor; and

at least one non-transitory memory storing instructions, which, when executed by the at least one hardware processor, cause the system to:

obtain a dataset that comprises a plurality of content sets;

generate multiple data representations defining the plurality of content sets by:

linking each content set of the plurality of content sets into one or more clusters, and

for each particular cluster, generate a respective data representation of the multiple data representations by summarizing respective content sets of the particular cluster;

identify, using a first artificial intelligence (AI) model set, at least one particular content set using a similarity value between the multiple data representations,

wherein the similarity value of the multiple data representations satisfies a constraint set;

generate, using a second AI model set, a reconfiguration command set configured to remove the at least one particular content set from the plurality of content sets; and

cause execution of the reconfiguration command set on the dataset to modify a portion of the dataset to remove the at least one particular content set from the plurality of content sets.

2 . The system of claim 1 , wherein the first AI model set is further configured to identify at least one content conflict between one or more pairs of data representations within the multiple data representations by:

linking a first data representation of the multiple data representations to (a) a topic and (b) a first information set,

linking a second data representation of the multiple data representations to (a) the topic and (b) a second information set, and

identify the content conflict between the first and second information sets by:

comparing the first and second information sets to determine a degree of similarity between the first and second information sets, and

determining that the degree of similarity fails one or more criterion.

3 . The system of claim 1 , wherein the system is further caused to:

cause transmission of a report indicating one or more of (a) the identified at least one particular content set or (b) the reconfiguration command set.

4 . The system of claim 1 , wherein the constraint set includes a threshold generated using a separate AI model that evaluates a degree of satisfaction of the threshold against a set of performance metrics.

5 . The system of claim 1 , wherein the at least one particular content set includes at least one duplicate content set.

6 . A non-transitory computer-readable storage medium comprising instructions stored thereon, wherein the instructions when executed by at least one data processor of a system, cause the system to:

obtain a dataset that comprises a plurality of content sets;

determine multiple data representations defining the plurality of content sets by:

linking each content set of the plurality of content sets into one or more clusters, and

for each particular cluster, generate a respective data representation of the multiple data representations by summarizing respective content sets of the particular cluster;

identify, using a first artificial intelligence (AI) model set, at least one particular content set using a similarity value between the multiple data representations,

wherein the similarity value of the multiple data representations satisfies a constraint set;

generate, using a second AI model set, a reconfiguration command set configured to remove the at least one particular content set from the plurality of content sets; and

cause execution of the reconfiguration command set on the dataset to modify a portion of the dataset to remove the at least one particular content set from the plurality of content sets.

7 . The non-transitory computer-readable storage medium of claim 6 , wherein the reconfiguration command set is generated based on a predefined rule set.

8 . The non-transitory computer-readable storage medium of claim 6 , wherein one or more AI models in the first AI model set is an AI agent.

9 . The non-transitory computer-readable storage medium of claim 6 , wherein the instructions further cause the system to:

detect a data linkage between portions of the dataset based on the one or more clusters; and

generate a representation of the data linkage in a form of one or more of: a knowledge graph, a tree structure, or a table.

10 . The non-transitory computer-readable storage medium of claim 6 , wherein the instructions further cause the system to:

map one or more content sets within the dataset to one or more organizational references associated with an organization.

11 . The non-transitory computer-readable storage medium of claim 6 , wherein the instructions further cause the system to:

receive feedback via an interface; and

modify one or more reconfiguration commands within the reconfiguration command set in accordance with the feedback.

12 . The non-transitory computer-readable storage medium of claim 6 , wherein the constraint set is associated with a majority vote of multiple AI models within the second AI model set.

13 . The non-transitory computer-readable storage medium of claim 6 ,

wherein the at least one particular content set comprises at least one content conflict between a first data representation and a second data representation, and

wherein the reconfiguration command set includes updating the first data representation or the second data representation.

14 . A computer-implemented method comprising:

obtaining a dataset that comprises a plurality of content sets;

determining multiple data representations defining the plurality of content sets;

identifying, using a first artificial intelligence (AI) model set, at least one particular content set using a similarity value between the multiple data representations,

wherein the similarity value of the multiple data representations satisfies a constraint set;

generating, using a second AI model set, a reconfiguration command set configured to remove the at least one particular content set from the plurality of content sets; and

causing execution of the reconfiguration command set on the dataset to modify a portion of the dataset to remove the at least one particular content set from the plurality of content sets.

15 . The computer-implemented method of claim 14 , wherein at least one of the first AI model set or the second AI model set includes an agent.

16 . The computer-implemented method of claim 14 , wherein the at least one particular content set comprises at least one of a content conflict or a duplicate content.

17 . The computer-implemented method of claim 14 , further comprising:

generating a compliance report indicating a compliance status of the dataset with predefined criteria.

18 . The computer-implemented method of claim 14 , further comprising:

maintaining a history of one or more data modifications to the dataset,

wherein each data modification is determined based on the modified portion of the dataset.

19 . The computer-implemented method of claim 18 , further comprising:

identifying one or more events associated with the at least one particular content set using one or more correlations between values of different data variables in the dataset.

20 . The computer-implemented method of claim 14 ,

wherein the modified portion of the dataset includes enriched data, and

wherein the enriched data replaces one or more missing values within the dataset based on reference data sources.