The commercial interest of solid state electrolyte materials has driven a range of studies which construct statistical models to predict the ionic conductivity of materials. These are typically used in screening purposes for assessing the potential conductivity of novel formulations before the lengthy process of synthesis. . Here we present one such model which aims to predict the ionic conductivity of candidate electrolyte materials based solely on composition. As synthesising a compound and then characterising its structure is both time consuming and error prone, it is advantageous to perform virtual screens of much broader compositional spaces before focusing on formulations of interest for further investigation. Regression and classification CrabNet models are trained on the compositions of the Liverpool ionics dataset (∼msd30/lmds/LiIonDatabase.html) to predict specific values of conductivity or whether a compound is likely to possess a conductivity greater than 10 -4 Scm -1 at room temperature. The interface takes in a chemical composition, or a comma separated list of compositions, processes these to be machine readable, generates predictions, and returns these predictions to the user as a downloadable table. This architecture and user interface can be extended into other domains by replacing the trained conductivity model with a bespoke model predicting other materials properties.