  The Bright Transient Survey (BTS) relies on visual inspection ("scanning") to
select sources for accomplishing its mission of spectroscopically classifying
all bright extragalactic transients found by the Zwicky Transient Facility
(ZTF). We present $\texttt{BTSbot}$, a multi-input convolutional neural
network, which provides a bright transient score to individual ZTF detections
using their image data and 14 extracted features. $\texttt{BTSbot}$ eliminates
the need for scanning by automatically identifying and requesting follow-up
observations of new bright ($m\,<18.5\,\mathrm{mag}$) transient candidates.
$\texttt{BTSbot}$ outperforms BTS scanners in terms of completeness (99% vs.
95%) and identification speed (on average, 7.4 hours quicker).
