Identifying semantic inference relations between texts is a major underlying language processing task, needed in practically all text understanding applications. For example, Question Answering and Information Extraction systems should verify that extracted answers and relations are indeed inferred from the text passages; multi-document text summarization needs to infer that one sentence entails another in order to avoid redundantly including both in a summary; and so on. While such apparently similar inferences are broadly needed, there are currently no generic semantic "engines" or platforms for broad textual inference. Rather, tools exist for narrow semantic tasks, but systems have to independently assemble and augment them to obtain a complete inference process. Changing this ineffective setting is our primary scientific motivation.Our second, industrial-oriented, motivation is within the text analytics market. We focus on the customer interaction domain, which today spans multiple channels such as speech, email and social media. This growing market shows increasing demand for automatically analyzing customer inputs to harness their value. A major stumbling block, however, is the current inability to perform effective inferences over complete customer statements, rather than just keyword-based or topical analysis.Accordingly, we set dual goals for our project. The first is to develop a generic multi-lingual platform for textual inference, based on the successful textual entailment paradigm, and make it available to the scientific and technological communities. This will enable diverse applications to leverage the open platform for their inference needs, while sharing around it the development of core semantic technology. Our second goal is to leverage the inference platform to develop a new generation of unsupervised text exploration technology for customer interactions, enabling business users to better grasp their diverse and often unpredicted content.