We present the DCU team’s system for the CASTLE Challenge at ACM Multimedia 2025, which explores video retrieval and question answering in egocentric, multi-user environments. Our system adapts techniques developed for lifelogging, particularly eventbased semantic retrieval and QA pipelines, to the CASTLE dataset with minimal architectural changes. It combines vision-language embeddings, transcript-based retrieval, and person tracking to support both automatic and interactive search workflows. In the interactive track, we introduce a modular interface for narrative reconstruction and exploratory search. Qualitative results show that the system can generate plausible, evidence-based answers to complex multimodal queries. These findings suggest that lifelog retrieval systems offer a viable foundation for broader egocentric video analysis.