NIR instruments in combination with multivariate data calibration models are useful tools in process monitoring. The calibration models often need to overcome matrix effects and must be re-calibrated over time to compensate for, e.g., changes in process parameters or raw materials, a task often involving manual expert assistance, which is a bottleneck for more widespread application of the technique. To ease the re-calibration, the BIOPRO consortium launched the “Hands free” project to research and develop automated methods for handling data sets and calibration of partial least squares (PLS) models. The intention was to find methods and algorithms which could be operated either by non-experts or fully automatically. Part of this effort was to develop a graphical user interface (GUI) application in MATLAB to enable rapid dissemination of methods to partners in the consortium and early testing by non-programmers. The resulting application, called Fulgur, can assist in handling near infrared (NIR) instrument calibration datasets, identifying outliers and developing and assessing PLS model performance with standard plots. Plots are focused on performance over time as it is usual to collect NIR data over longer time spans when monitoring industry processes. In this article, we present an overview of the application for end-users and explain the outlier detection and model performance plotting in more depth. Source code for the application together with a more in-depth user guide is available for download from models.life.ku.dk. In the next article, we will introduce the source code for potential developers and the principles behind the programming architecture used to develop the application.