Techniques for optimizing project data storage

1 . A system for optimizing project data storage comprising:

one or more processors; and

one or more memories communicatively coupled with the one or more processors,

the one or more memories storing a machine learning (ML) model and computer executable instructions that, when executed by the one or more processors, cause the one or more processors to:

execute the ML model to determine a predicted data category mapping that maps a first data category to a normalized data category,

execute, based on the predicted data category mapping, a set of instructions configured to:

input the first data category into a first table having a first file size, and

collapse the first table with a second table by adjusting identification values associated with (i) the first data category or (ii) a second data category in a second table having a second file size into a path value that references both the first data category and the second data category and is stored in a third table having a third file size based on the path value that is less than a combination of the first file size and the second file size, and store the third table in a project database.

2 . The system of claim 1 , wherein the computer executable instructions, when executed by the one or more processors, further cause the one or more processors to:

receive an external input corresponding to a project with project data stored in the project database;

execute a second ML model trained to:

extract external data from the external input, and

analyze the external data to output (i) a predicted classification and (ii) a predicted impact associated with the project; and

cause a user computing device to display the predicted classification and the predicted impact to a user.

3 . The system of claim 2 , wherein the computer executable instructions, when executed by the one or more processors, further cause the one or more processors to:

determine a classification of the external input based on the predicted classification;

update an entry of a data category within the third table based on the classification; and

adjust an estimated value within the third table based on the predicted impact.

4 . The system of claim 1 , wherein the computer executable instructions, when executed by the one or more processors, further cause the one or more processors to:

analyze one or more external inputs to identify request data included as part of the one or more external inputs;

extract the request data from the one or more external inputs;

execute an artificial intelligence (AI) model configured to identify whether the request data conforms to each of a set of consolidation rules; and

responsive to determining that the request data conforms to each of the set of consolidation rules, generate, using the request data, a draw request that is formatted based on the set of consolidation rules.

5 . The system of claim 4 , wherein the one or more external inputs are in a plurality of different formats when analyzed by the one or more processors, and the computer executable instructions, when executed by the one or more processors, further cause the one or more processors to:

convert the one or more external inputs from the plurality of different formats to a normalized format.

6 . The system of claim 1 , wherein the computer executable instructions, when executed by the one or more processors, further cause the one or more processors to:

render a project projection graphical user interface (GUI) configured to display data stored within the third table;

receive updated project data;

determine that the updated project data violates a threshold value corresponding to a respective data category represented in the project projection GUI; and

adjust a graphical display element of the project projection GUI corresponding to the respective data category based on the updated project data violating the threshold value.

7 . The system of claim 1 , wherein the computer executable instructions, when executed by the one or more processors, further cause the one or more processors to:

predict, by executing a second ML model, a retainage value based on data included in the third table;

receive updated data to be stored in the third table;

generate, by executing the second ML model using the updated data as inputs, an updated retainage value; and

cause a user computing device to display the updated retainage value to a user.

8 . The system of claim 1 , wherein the computer executable instructions, when executed by the one or more processors, further cause the one or more processors to:

generate an estimated project completion value based on data included in the third table;

transmit the estimated project completion value to an external computing device;

receive, from the external computing device, an adjusted project completion value; and

determine an updated project completion value based on the estimated project completion value and the adjusted project completion value.

9 . The system of claim 1 , wherein the computer executable instructions, when executed by the one or more processors, further cause the one or more processors to:

cause a user computing device to render a project graphical user interface (GUI) that includes the third table for access by a user of the user computing device; and

responsive to receiving input from the user, execute the set of instructions to expand the third table into the first table and the second table.

10 . The system of claim 1 , wherein the set of instructions are a set of nesting instructions, the third table is a nested table, and the second data category in the second table is related to the first data category.

11 . The system of claim 1 , wherein the ML model is a trained ML model that is trained using a plurality of training data categories and a plurality of training normalized data categories as input to output a plurality of training predicted data category mappings.

12 . A computer-implemented method for optimizing project data storage comprising:

determining, by one or more processors executing a machine learning (ML) model, a predicted data category mapping that maps a first data category to a normalized data category;

executing, by the one or more processors and based on the predicted data category mapping, a set of instructions configured to:

input the first data category into a first table having a first file size, and

collapse the first table with a second table by adjusting identification values associated with (i) the first data category or (ii) a second data category in a second table having a second file size into a path value that reference both the first data category and the second data category and is stored in a third table having a third file size based on the path value that is less than a combination of the first file size and the second file size; and

storing, by the one or more processors, the third table in a project database.

13 . The computer-implemented method of claim 12 , further comprising:

receiving, at the one or more processors, an external input corresponding to a project with project data stored in the project database;

executing, by the one or more processors, a second ML model trained to:

extract external data from the external input, and

analyze the external data to output (i) a predicted classification and (ii) a predicted impact associated with the project;

determining, by the one or more processors, a classification of the external input based on the predicted classification;

updating, by the one or more processors, an entry of a data category within the third table based on the classification;

adjusting, by the one or more processors, an estimated value within the third table based on the predicted impact; and

causing, by the one or more processors, a user computing device to display the predicted classification and the predicted impact to a user.

14 . The computer-implemented method of claim 12 , further comprising:

analyzing, by the one or more processors, one or more external inputs to identify request data included as part of the one or more external inputs, wherein the one or more external inputs are in a plurality of different formats;

extracting, by the one or more processors, the request data from the one or more external inputs;

converting, by the one or more processors, the one or more external inputs from the plurality of different formats to a normalized format;

executing, by the one or more processors, an AI model configured to identify whether the request data conforms to each of a set of consolidation rules; and

responsive to determining that the request data conforms to each of the set of consolidation rules, generating, by the one or more processors using the request data, a draw request that is formatted based on the set of consolidation rules.

15 . The computer-implemented method of claim 12 , further comprising:

rendering, by the one or more processors, a project projection graphical user interface (GUI) configured to display data stored within the third table;

receiving, at the one or more processors, updated project data;

determining, by the one or more processors, that the updated project data violates a threshold value corresponding to a respective data category represented in the project projection GUI; and

adjusting, by the one or more processors, a graphical display element of the project projection GUI corresponding to the respective data category based on the updated project data violating the threshold value.

16 . The computer-implemented method of claim 12 , further comprising:

predicting, by the one or more processors executing a second ML model, a retainage value based on data included in the third table;

receiving, at the one or more processors, updated data to be stored in the third table;

generating, by the one or more processors executing the second ML model using the updated data as inputs, an updated retainage value; and

causing, by the one or more processors, a user computing device to display the updated retainage value to a user.

17 . The computer-implemented method of claim 12 , further comprising:

generating, by the one or more processors, an estimated project completion value based on data included in the third table;

transmitting, by the one or more processors, the estimated project completion value to an external computing device;

receiving, at the one or more processors from the external computing device, an adjusted project completion value; and

determining, by the one or more processors, an updated project completion value based on the estimated project completion value and the adjusted project completion value.

18 . The computer-implemented method of claim 12 , further comprising:

causing, by the one or more processors, a user computing device to render a project graphical user interface (GUI) that includes the third table for access by a user of the user computing device; and

responsive to receiving input from the user, executing, by the one or more processors, the set of instructions to expand the third table into the first table and the second table.

19 . A tangible non-transitory machine-readable medium comprising instructions that, when executed, cause a machine to at least:

execute a ML model to determine a predicted data category mapping that maps a first data category to a normalized data category;

execute, based on the predicted data category mapping, a set of instructions configured to:

input the first data category into a first table having a first file size, and

collapse the first table with a second table by adjusting identification values associated with (i) the first data category or (ii) a second data category in a second table having a second file size into a path value that references both the first data category and the second data category and is stored in a third table having a third file size that is less than a combination of the first file size and the second file size; and

store the third table in a project database.

20 . The tangible non-transitory machine-readable medium of claim 19 , wherein the instructions, when executed, further cause the machine to at least:

receive an external input corresponding to a project with project data stored in the project database;

execute a second ML model trained to:

extract external data from the external input,

analyze the external data to output (i) a predicted classification and (ii) a predicted impact associated with the project, and

determine a classification of the external input based on the predicted classification;

update an entry of a data category within the third table based on the classification;

adjust an estimated value within the third table based on the predicted impact; and

cause a user computing device to display the predicted classification and the predicted impact to a user.