SUMMARY:

Procesus, a spin-off of STATUM, launched a service that allows any company to create its own automated front desk powered by artificial intelligence. The tool is cloud-based and combines various modules from the Apia ecosystem, such as MDS, ApiaDocumentum, and ApiaTrámites.
 

INFORMATION:

The MDS system was designed to centralize and automate the receipt and management of tickets within an organization. When a user sends an email to a support inbox, the system automatically generates a ticket, notifies the sender, and assigns a tracking number.

This process eliminates manual intervention and ensures that each request is recorded and managed in an organized manner. Automation also reduces the margin of error and prevents inquiries from being lost or left unanswered. Each company can define specific workflows for its tickets, adapting the processes to its operational structure. This allows for more efficient service, with better control and tracking of incidents and requests.

The system centralizes all information and communications in a single environment, facilitates collaboration between teams, and keeps records up to date. This integration improves transparency and accelerates response times.

How to create an AI-powered help desk

Creating a cloud-based help desk requires no technical expertise. The user simply enters a name for the desk, a description, and the organization it belongs to. With these three pieces of information, the system automatically generates the desk and makes it ready for use.

Once created, the system can be configured to receive emails in one or more support inboxes. From that moment on, MDS automatically begins logging all requests and managing the support workflow.

The tool incorporates support models based on ITIL, the English standard that brings together decades of knowledge on service management. These models encompass processes for change management, problem resolution, inquiries, and service requests.

The artificial intelligence developed by STATUM is capable of analyzing the organization's big data and understanding its operational structure. With this information, the system proposes support models tailored to the actual needs of the business.

The result is a service environment that learns from historical data, improves service flows, and optimizes workload distribution across different teams.