The Tavel Lab will soon be commercially releasing version 1.0 of the Avatar Machine Learning Improvisational Assistant, developed by Jason Palamara and Scott Deal. The Avatar program is a machine-learning-enabled “choice engine” which provides a dynamically sensitive duet while listening to live vibraphone performances. The initial version is geared for use with a vibraphone, with additional instruments soon to follow. Using this system, the musician performs improvisations on the vibraphone while the software listens, closely following the vibraphone performance. The package employs a Markov-chain model culled from Scott Deal’s improvisations. This mindfile database allows the software to generate novel content based on Scott Deal’s style. While the Markov transition database provides note-to-note transitions, the AvatarPlayer makes use of this data in several ways. Throughout a performance, the AvatarPlayer cycles through five playback behaviors (favor repetition, favor novelty, favor four notes, favor chords, and favor phrases), all of which make use of the database differently.