Any classification or regression model needs access to the same features or input that were utilized to train the model. However in real world scenarios, several models are in operation for years and in those cases new variables/features may be available during the inferencing stage. If such features are to be utilized their values have to be captured in the dataset that was utilized for training the model. We propose a model agnostic approach where we trained a model without the access to those features during the training stage, which could benefit from the additional features available during testing. We show that by using the proposed approach and without any access to the extra features during the training phase, we are able to improve the performance of the model on four real world tabular datasets. We provide extensive analysis on how and which variables result in the improvement over the model which was trained without the extra feature.