  A suite of spectroscopic surveys is producing vast sets of stellar spectra
with the goal of advancing stellar physics and Galactic evolution by
determining their basic physical properties. A substantial fraction of these
stars are in binary systems, but almost all large-survey modeling pipelines
treat them as single stars. For sets of multi-epoch spectra, spectral
disentangling is a powerful technique to recover or constrain the individual
components' spectra of a multiple system. So far, this approach has focused on
small samples or individual objects, usually with high resolution ($R \gtrsim
10.000$) spectra and many epochs ($\gtrsim 8$). Here, we present a
disentangling implementation that accounts for several aspects of few-epoch
spectra from large surveys: that vast sample sizes require automatic
determination of starting guesses; that some of the most extensive
spectroscopic surveys have a resolution of only $\approx 2,000$; that few
epochs preclude unique orbit fitting; that one needs effective regularisation
of the disentangled solution to ensure resulting spectra are smooth. We
describe the implementation of this code and show with simulated spectra how
well spectral recovery can work for hot and cool stars at $R \approx 2000$.
Moreover, we verify the code on two established binary systems, the ``Unicorn''
and ``Giraffe''. This code can serve to explore new regimes in survey
disentangling in search of massive stars with massive dark companions, e.g. the
$\gtrsim 200,000$ hot stars of the SDSS-V survey.
