Qualitative research emphasizes constructing meaning through iterative engagement with textual data. Traditionally, this human-driven process requires navigating coder fatigue and interpretive drift, thus posing challenges when scaling analysis to larger, more complex datasets. Computational approaches to augment qualitative research have been met with skepticism, partly due to their inability to replicate the nuance, context-awareness, and sophistication of human analysis. LLMs, however, present new opportunities to automate aspects of qualitative analysis while upholding rigor and research quality. In this work, we present and benchmark Muse, an interactive qualitative research system that allows researchers to identify themes and annotate datasets, achieving an inter-rater reliability between Muse and humans of Cohen’s 𝜅 = 0.7 for well-specified codes.