Software Maintenance and Upgrade

“We set strict requirements for IT security and system performance”
Customer voice

To ensure that your PROGNOST® system always works securely and reliably, regular Software Maintenance is essential. Software Maintenance is an integral part of the system health to protect your investment against obsolescence and ensures system stability.

PROGNOST continuously develops enhancements to improve the system efficiency, operational and IT security and to optimize monitoring reliability. Service packs optimize technical software characteristics, and design while keeping the interface consistent. This will prevent the system from any upcoming vulnerability due to cyber-crime or incompatibility.

A Service Pack can include new failure patterns, new hardware driver or updates to address new issues such as security.
Software Maintenance excludes new features and functionalities like new analyses, which are considered as Software Upgrades or System Extensions. which add additional functions or expand your system’s capabilities.

Software Maintenance optimizes the system performance by eliminating issues and applying advanced development.

The fundamental best practice for maintaining monitoring systems: Be proactive, not reactive, about performing updates and patching. If you are not patching on a regular basis, the system becomes obsolete over time.

We recommend all our customers to keep their systems up-to-date by contracting Software Maintenance.

Advantages

With Software Maintenance, your system is kept up-to-date with latest technologies to run with high-end efficiency. Service packs can be obtained easily via the Auto Updater directly from the user client software or by contacting your PROGNOST customer support team.

PROGNOST gathers annual experience of +11 million machine operating hours including all new failures happened within. The essence of this data collection is available as new and refined Failure Patterns added to your diagnostic database so your system receives the key learnings from +1,400 machines.