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[FIX ME: make me a happy, positive statement] While these exacting steps are worthwhile for the quality fitting results, gathering, pre-processing, and converting data to plug it in to a fitting engine should be standard/interpoerable and straightfoward.   This inconvenience -- building SEDs from multiple sources -- drove a part of Iris' SED analysis design. Following VO efforts to seamlessly combine data services and applications, Iris offers a standard means of building large broadband SEDs from different sources in various data formats, while providing robust fitting methods and interactive visualization capabilities. Users can input SED data from a local file or a URL, with high leniency on the data format. Users may also beam data from other VO-enabled applications or data archive services through SAMP (Simple Application Messaging Protocol; \citet{http://adsabs.harvard.edu/abs/2011arXiv1110.0528T}). \citet{2011arXiv1110.0528T}).  If the data format follows the IVOA Spectrum Data Model v1.03 \citep{2012arXiv1204.3055M}, or in other words a VO-compliant VOTable or FITS file, the data is read-in without any input by the user; otherwise, the user supplies the units and mapping to the spectral-flux coordinates in the file. How this method works is described in Section \ref{sec:components}. Iris is meant to be an open box SED analysis tool. If Iris is missing a certain functionality, the user may develop a plugin for that functionality and add it to Iris. Thus, a user can employ Iris' SED building capabilites while running custom analysis, which in theory could be one of the fitting packages discussed above. We explore this possibility in detail in Section \ref{sec:plugins}