The field of bioacoustics has rapidly evolved in the last decade in terms of hardware, statistical methods, and automatic classification of sounds. Despite this, wildlife professionals are increasingly faced with large volumes of audio and limited options for scaling up the processing and review of sound data that do not require technical expertise. Our solution to this problem is AudioDash, an open-source, cloud-hosted, platform that enables users to leverage the power of well-known audio classification models such as BirdNET and Perch, through a browser-based graphical interface that requires no programming knowledge. Current features include the ability to: process audio through BirdNET or Perch classifiers, generate audio selections for manual review based on classifier output, and build and refine custom Perch classifiers tailored to species of interest. To demonstrate the capacity of AudioDash we provide three project examples: a multi-species bird study, a study of a single endangered bird species, and a custom classifier for vehicle sounds. We aim to bridge the gap between the technical innovations taking place in bioacoustics, such as cutting-edge machine learning models, and practicing wildlife professionals who may not have the time or technical expertise to implement these new technologies at scale.