RIFT, short for **Relational Information Feedback Time**, is a conceptual information-processing framework based on the hypothesis that time is not a standalone phenomenon, but a relational structure emerging from feedback among events. In this view, an isolated event remains close to an unresolved possibility, while an event embedded in an environment becomes part of a feedback system. As events influence one another, the effective computational burden of predicting future states can grow explosively. This may help explain why complex real-world systems, from weather to particle-scale aggregates and financial markets, become difficult to predict even when their local interactions appear simple. RIFT further proposes that attention, or observation, can be interpreted as an information-field effect: attention assigns informational weight to events and can distort the distribution of entropy-like uncertainty, analogously to how gravity curves spacetime. This paper introduces RIFT at a high conceptual level and outlines its possible applications in event-driven computation, adaptive sensing, market microstructure analysis, control systems, and real-time decision engines. A small experimental system, referred to as **RIFT Scalper**, is described as an applied research prototype using real-time order-flow data. No proprietary implementation details, trading rules, parameters, or performance-sensitive mechanisms are disclosed.