  Hot dust in the proximity of AGNs strongly emits in the Near Infrared
producing a red excess that, in type 2 sources, can be modeled to measure its
temperature. In the era of high spatial-resolution multi-wavelength data,
mapping the hot dust around Supermassive Black Holes is important for the
efforts to achieve a complete picture of the dust role and distribution around
these compact objects.
  In this work we propose a methodology to detect the hot dust emission in the
proximity of Type 2 AGNs and measure its temperature using K-band spectra
($\lambda_c$ = 2.2\,$\mu$m).
  To achieve this, we have developed NIRDust, a Python package for modeling
K-band spectra, estimate the dust temperature and characterize the involved
uncertainties. We tested synthetic and real spectra in order to check the
performance and suitability of the physical model over different types of data.
  Our tests on synthetic spectra demonstrated that the obtained results are
influenced by the signal-to-noise ratio (S/N) of the input spectra. However, we
accurately characterized the uncertainties, which remained below $\sim$150 K
for an average S/N per pixel exceeding 20. Applying NIRDust to NGC 5128
(Centaurus A), observed with the Gemini South Telescope, we estimated a dust
temperature of 662 and 667 K from Flamingos-2 spectra and 697 and 607 K from
GNIRS spectra using two different approaches.
