Artificial intelligence (AI) and data monitoring are working together to digitally transform relationships, businesses, and people. In telecommunications, predictive analysis based on data collection plays a crucial role in development. Starting with version 6.0 of Zabbix, users have benefited from updates in predictive functions and machine learning, which make it possible for them to study the data monitored by Zabbix and integrate it with AI modules.

Danilo Barros, co-founder of Lunio (a Zabbix Certified Partner in Brazil), presented the results of using Zabbix combined with telecom data monitoring through AI and machine learning at Zabbix Conference Brazil in 2022. Keep reading to get the whole story!

The scenario

With over 600 OLTs (Optical Line Terminals – the fiberoptic infrastructure used by internet providers) as well as 400,000 customers across more than 800 cities and 20 states in Brazil, Lunio’s client manages a staggering amount of data. This monitoring is essential for smooth operations and to guarantee that there are no negative impacts on users and no overload for customer service agents in the event of accidents.

A primary challenge for telecom clients is the overload of calls to customer service in the event of massive network incidents. With so many customers, every precaution must be taken to avoid clogging phone lines during outages or service failures.

“You can’t achieve customer satisfaction under such circumstances, and the Net Promoter Score (NPS) drops drastically.”


Danilo Barros, co-founder of Lunio

Mapping needs

Considering the client’s operational structure, a series of customer needs were identified, focusing on six main points:

1. Automation: With notifications via digital channels for each event
2. Speed: Aiming for improved customer service
3. Operational costs: Budget optimization
4. Root cause analysis: Quick identification of the cause of events
5. Predictability: The ability to analyze problems and identify trends
6. Reporting: Identifying incidents and following regulations from ANATEL (National Telecommunications Agency)

With these interests in mind, it was possible to reassess the use of tools previously employed by the telecom client, which at the time served unique functions in the process. Each tool had its usage and information verification time, which could impact hundreds of users in a massive-scale incident. The key challenges identified by the Lunio team included:

  • Integrations: Systems needed to be interconnected
  • Integrity: Constant data updates
  • Topology: With system mapping through specific programs
  • Business rules: Respecting the development of local processes
  • Performance: The monitoring and automation of 600,000 assets
  • High availability: Dozens of data centers catering to local demand

Once the needs and challenges were identified, it was time to promote change within the client. By integrating systems and using Zabbix to monitor over 600,000 items, understand incidents, and predict potential future errors, the technical teams at Lunio created LunioAI, a “super attendant” with analytical and predictive capabilities as well as the ability to continuously learn.

“This guy (LunioIA) learns from each event, understanding each topology that occurs in the client’s network.”


Danilo Barros, co-founder of Lunio

In the initial response tests, LunioAI was able to analyze and evaluate massive events in a minute and a half. Over time, this was reduced to 30 seconds, making the return to the technical team increasingly swift and positively impacting incident resolution.

The results

Throughout the development and improvement of LunioIA, the operations chain was involved in predictive analyses of potential events on the network, providing technical professionals with the information needed to perform preventive maintenance on monitored items.

LunioIA considers data from integrated systems, FTTH (fiber to the home) environments, data centers, and items, all as part of the Zabbix monitoring environment. It can then diagnose events, understand the severity of an event, and find resolution points – without the need for human resources in the process.

As a result, when physical attendants were contacted by customers experiencing difficulties with the service, instead of going through the entire process to understand what happened, the attendant could perform a search using the customer’s CPF (Individual Taxpayer Registry Identification) and then access a summary of the events, causes, and solutions identified by artificial intelligence combined with data monitoring through Zabbix.

In conclusion

This example happens to come from the telecommunications industry, but it’s not difficult to see how the ability of Zabbix to integrate the data monitored by Zabbix with AI modules can benefit companies in almost any industry.

You can find out more about what we can do across a variety of industries by visiting our website or requesting a demo.

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