TESS is a powerful engine for studying stellar variability. However, it has been challenging to adopt methods used for other space missions, such as Kepler, to infer rotation periods and solar-like oscillation frequencies in TESS data. The availability of multiple sectors of data for most targets, improved stability, and the release of new calibrated datasets such as the Quick Look Pipeline (QLP) have dramatically improved the situation. In this presentation, we will show how we process the TESS data starting from QLP datasets and processing them with the Py-TADACS software, a python package based on the KEPLER asteroseismic optimized KADACS software. We present results on three test cases, relevant for samples of hundreds of thousands of stars: solar-like oscillations and granulation signals in red giants, and rotation periods in solar-like stars. We compare our results with previous studies and discuss yield statistics.