The use of the walk-over-weighing (WoW), which automatically records animal live weight (LW) in an automated, non-invasive manner, involves filtering the primary datasets produced by this technology. Removing outliers allowsthe correct data to be retained for a more consistent interpretation of individual daily physical activity progression. However, the standard methods used so far to perform this cleaning were impractical, time-consuming and required minimal mastery of the methods used. This limits the adoption of WoW by farmers and other end users. Our team previously developed a Kalman filter with impulse noised outliers algorithm for the automatic detection of outliers generated by the WoW (kfino; https://arxiv.org/abs/2208.00961). Once the kfino algorithm was tuned, the ORIOLE web-application was developed and deployed by our team (for OutlieRs detectIOn waLk wEighing; https://oriole.sk8.inrae.fr/). The Shiny library of the R software which enables to easily create user-friendly interactive web apps straight from R was used (https://shiny.rstudio.com/). Our web application allows users to import raw data measured from the WoW and through simple settings to perform outlier detection and weight prediction during the experiment. Descriptive statistics are then available such as number of daily weighing, evolution of weight per animal, evolution of the flock weight, 24 h kinetics of individuals. The web app is a dashboard composed of a menu of several subsets offering a user-friendly experience: (1) a ‘Welcome’ section; (2) the ‘Genesis’ of the technology and the web-app project; (3) the heart of the app with a section for the import and analysis of ‘WoW data’ and producing useful reports; (4) a ‘How to’ section documenting how to use the app. Users can analyse their data using full advantage of descriptive and statistics plots and download reports for communication and decision making.