Systems and methods for managing completed jobs associated with a plurality of customers
In various embodiments, the present disclosure relates to managing completed jobs associated with a plurality of customers. Steps include training a machine learning model with customer data associated with one or more customers of an infrastructure service provider; parsing data related to one or more jobs, wherein the one or more jobs are associated with the one or more customers of the infrastructure service provider; determining, via the machine learning model, that one or more of the jobs are completed; and performing an action based on the determining.
1 . A non-transitory computer-readable medium comprising instructions that, when executed, cause one or more processors to perform steps of:
training a machine learning model with customer data associated with one or more customers of an infrastructure service provider;
parsing data related to one or more jobs, wherein the one or more jobs are associated with the one or more customers of the infrastructure service provider;
determining, via the machine learning model, that one or more of the jobs are completed; and
performing an action based on the determining.
2 . The non-transitory computer-readable medium of claim 1 , wherein the one or more actions include any of automatically sending an invoice to a customer and notifying an invoicing team.
3 . The non-transitory computer-readable medium of claim 1 , wherein the data includes one or more files, and wherein the machine learning model is adapted to identify files related to the completion of a job.
4 . The non-transitory computer-readable medium of claim 1 , wherein the action includes sending an invoice for jobs determined to be completed, and wherein the steps further comprise:
sending a follow up notification.
5 . The non-transitory computer-readable medium of claim 4 , wherein sending the follow up notification is configured to occur at a particular time, and wherein the machine learning model is adapted to determine a particular time based on historical payment behaviors of the one or more customers.
6 . The non-transitory computer-readable medium of claim 5 , wherein the steps comprise grouping invoices associated with a specific customer and sending a follow up notification for the group of invoices at a particular time.
7 . The non-transitory computer-readable medium of claim 1 , wherein the training includes any of supervised and unsupervised learning.
8 . The non-transitory computer-readable medium of claim 1 , wherein the customer data includes historical customer data.
9 . The non-transitory computer-readable medium of claim 1 , wherein the steps further comprise:
generating a closeout package for jobs determined to be completed.
10 . A method comprising steps of:
training a machine learning model with customer data associated with one or more customers of an infrastructure service provider;
parsing data related to one or more jobs, wherein the one or more jobs are associated with the one or more customers of the infrastructure service provider;
determining, via the machine learning model, that one or more of the jobs are completed; and
performing an action based on the determining.
11 . The method of claim 10 , wherein the one or more actions include any of automatically sending an invoice to a customer and notifying an invoicing team.
12 . The method of claim 10 , wherein the data includes one or more files, and wherein the machine learning model is adapted to identify files related to the completion of a job.
13 . The method of claim 10 , wherein the action includes sending an invoice for jobs determined to be completed, and wherein the steps further comprise:
sending a follow up notification.
14 . The method of claim 13 , wherein sending the follow up notification is configured to occur at a particular time, and wherein the machine learning model is adapted to determine a particular time based on historical payment behaviors of the one or more customers.
15 . The method of claim 14 , wherein the steps comprise grouping invoices associated with a specific customer and sending a follow up notification for the group of invoices at a particular time.
16 . The method of claim 10 , wherein the training includes any of supervised and unsupervised learning.
17 . The method of claim 10 , wherein the customer data includes historical customer data.
18 . The method of claim 10 , wherein the steps further comprise:
generating a closeout package for jobs determined to be completed.