Quality inspection of existing buildings is a task currently performed by human inspectors. In general, these inspections consist of assessing the different elements of a building as they are being constructed, checking that they are within acceptable tolerances, and meeting industry standards. Typically, this process is carried out by doing a visual inspection, taking photographs, and using measuring tools to identify deficiencies for further comparison with the BIM model. The acquired data must be analyzed by different specialists such as civil, electrical, and mechanical engineers, looking for defects or substandard installations. This process is time-consuming and dependent on the human factor, leading to errors and inconsistencies. To counteract that, we propose a methodology based on a multi-robot system that works synergistically to automatically collect data and analyze it for the further generation of a quality report. By automating the process, we are making the quality inspection more reliable, robust, and time-efficient. The master robot will collect general data and identify specific regions of interest (ROI) (e.g., potentially defective areas). When additional information is needed, the master robot will command the slave robot to approach the ROI to collect more detailed data. This can be used to inspect some of the most prevalent defects in construction sites, such as cracks, hollowness in walls (i.e., lack of insulation or incomplete concrete fillings), surface finishing defects, alignment errors, evenness, inclination deviations, and possibly more.