Startups need investments in order to scale their business. The value of such startups, especially software-based startups, are difficult to evaluate because there is no physical value that can be judged.  The company DueDive built experience in due diligence by conducting many interviews in this area, which are the base for the due diligence. These interviews are time consuming and require a lot of domain knowledge in the field, which makes them very expensive. This thesis evaluated different machine learning algorithms to integrate into a software that supports such interviews process. The goal is to shorten the interview duration and lowering the required know know for the interviewer using suggestions by the AI. The software uses completed interview sessions to provide enhanced suggestions through artificial intelligence. The proposed solution uses basket analysis and imputation to analyze the collected data. The result is a topic-independent software that is used to administrate and carry out interviews with the help of AI. The results are validated and evaluated in a case study using a generic, self-defined interview.