Organizations increasingly rely on Process Performance Indicators (PPIs) to evaluate how effectively their business processes achieve strategic and operational goals. Recent approaches compute PPIs directly from event logs leveraging the increasing availability of event data in organizations, but they assume that PPIs have been previously defined using well-established reference models and that their linkage to the event log has been explicitly specified. However, this definition of appropriate PPIs remains challenging, as managers often lack detailed insights into real process executions and must adapt generic frameworks to organizational contexts. To address this gap, this paper introduces PPIPilot, an approach and tool that supports managers in defining measurable PPIs by suggesting a set of relevant PPIs and their computations directly from a business process description, an associated event log, and a specified organizational goal. PPIPilot leverages large language models for contextual PPI suggestion and structured translation, operating through a three-component pipeline of suggestion, translation, and computation, with a fallback mechanism to address computation errors. The quantitative evaluation shows that PPIPilot has a strong ability to suggest and compute PPIs, and that the fallback mechanism reduces failures, increasing the share of PPIs that can be computed without errors. Complementing these findings, an in-depth qualitative study indicates that users find the tool intuitive, transparent and helpful for easing both the conceptual and technical challenges of defining PPIs.