Note: This feature is currently in Beta and available only to select customers. Sign up here if you're interested to try it out.


Automated Grouping is a process that uses Machine Learning algorithms to build contextually rich incidents while simultaneously reducing noise.

The challenge: Contextually poor incidents creating noise and increasing MTTR

Typically, monitoring tools continuously generate notifications of all kinds. Not all of them are actionable because they could include:

  • Status updates

  • Repetitive notifications

  • Notifications that lack context in the form of resource or application information

Incidents created from such alerts – even when grouped on the basis of resource and metric – lack sufficient context. Moreover, multiple incidents related to common issue create noise, making it difficult to identify the root cause leading to an increase in the Mean Time To Resolve (MTTR). 

The solution: Association of fresh incoming alerts to relevant open incidents

Automated Grouping employs Freshservice’s proprietary Machine Learning algorithm, Freddy, to study incoming alerts and attach them to related open incidents. If there is no relevant open incident, Freddy creates a new incident.

This connection is based on historical behaviour of an organization’s digital infrastructure. Freddy studies the kinds of alerts clubbed together to refer to a common issue in the past. It identifies patterns in the historical data which it looks for in fresh alerts while correlating them with open incidents.

Freddy can also be trained to understand patterns by manually attaching an alert to an open incident and establishing a correlation. Similarly, an incorrect correlation can be pointed out by manually detaching a Freddy-attached alert from an incident. 

When suitably trained, Freddy reduces noise up to 50% and makes incidents contextually richer. Such incidents provide a real-time picture of an issue, making it easier for the NOC and DevOps teams to make fast and effective decisions.

A solution that grows more perceptive with time

This continuous learning builds the Freddy's repository of alert and incident patterns unique to an organization. The algorithm then uses these patterns to suppress unimportant alerts, group together notifications that are indicative of an issue, and attach incoming alerts to open incidents. With Automated Grouping, noise is reduced while contextually rich incidents enable NOC & DevOps teams to detect and address issues faster. 

Note: Automated Grouping is available only to accounts with a large number of notifications and resources. Freshservice will notify you if you qualify to use this feature. You will then need to ‘enable’ Automated Grouping from the Admin pane in the tool.