The ask

MetTel is a telecommunications company providing data, network, cloud, and mobile IT solutions for businesses and government agencies. Like many large companies, MetTel has a trouble ticket system in place to respond to customer needs. Customers would submit repair request emails about any issues they were having with their services and an operator or engineer would manually raise, sort, and resolve each ticket.


To improve their operational efficiency and quality of service, MetTel were looking for ways to capitalize on this existing investment by enhancing it with new capabilities. More specifically, they were curious how A.I. and process automation could be used to execute the identification of issues, the creation of tickets, and the day-today repetitive maintenance tasks of monitoring and triage of network devices (SD-WAN).

Key objective: Improve operational efficiency and quality of service by enhancing an existing ticketing system.

What we did

A large volume of the corrective tasks required to bring a device back into operation involves a complex triage operation to analyze the reason for failure. It would take a member of the support team an average of 10-15 minutes on each device to gather enough information to make a decision on how to proceed.


To radically improve performance and speed up processes, we created Ticket Next Best Action (TNBA) – a solution that uses programming techniques to automate each stage of the issue resolution process, and A.I. to identify and execute the best possible next action using historical data.




The process

Step one

We began by pulling together historical data from a range of disparate internal tools and logs, enabling us to analyze the current and previous status of affected systems, and create classifications for errors.


Step two

Next we built the functionality to integrate MetTel’s APIs allowing us to  draw in real-time data from live tickets. This allows tickets to be appropriately classified as they are received, and either allocated to the best resolution team or analyzed by TNBA’s A.I. capabilities.


Step three

Finally, we defined the rules for all live data, instructing the system on how to handle and process tickets once they have been classified.


Within these process chains, TNBA was able to accurately predict the first stage of the ticket 75% of the time. A further 60% of the time, it confidently predicts the next stage of the ticket and, if applicable, automatically moves it along to solve the customer’s issue.


A further 20% of the time, the solution correctly suggests the next stage to an operator, helping to save time and drive further efficiency. All triage of SD-WAN is now fully automated and 52% of corrective maintenance operations are now completely solved by A.I predictions. This allows MetTel to dedicate valuable resources into offering more proactive services, and to drive the continous growth of their company without a significant increase in the department’s budget.


By building upon MetTel’s existing technology with A.I. enabled process automation, TNBA provided the required efficiency improvements without the financial investment and operational disruption that a full system overhaul would demand.


Hear from CTO at MetTel Ed Fox and Intelygenz’ COO Chris Brown as they discuss how we helped MetTel with AI.

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