Women who are diagnosed with breast cancer are referred to Neoadjuvant Chemotherapy Treatment (NACT) before surgery when treatment guidelines indicate that. Achieving complete response in this treatment is correlated with improved overall survival compared with those experiencing a partial or no response at all. In this paper, we explore multi modal clinical and radiomics metrics including quantitative features from medical imaging, to assess in advance complete response to NACT. Our dataset consists of a cohort from Institut Curie with 1383 patients; from which 528 patients have mammogram imaging. We analyze the data via image processing, machine learning and deep learning algorithms to increase the set of discriminating features and create effective models. Our results show ability to classify the data in this problem settings, using the clinical data. We then show the possible improvement we may achieve in combining clinical and mammogram data measured by the AUC, sensitivity and specificity. We show that for our cohort the overall model achieves sensitivity 0.954 while keeping good specificity of 0.222. This means that almost all patients that achieved pathologic complete response will also be correctly classified by our model. At the same time, for 22% of the patients, the model could correctly predict in advance that they won't achieve pathologic complete response, enabling them to reassess in advance this treatment. We also describe our system architecture that includes the Biomedical Framework, a platform to create configurable reusable pipelines and expose them as micro-services on-premise or in-thecloud.