Multimodal analysis in AI-moderated research refers to the integrated examination of verbal, vocal, and visual signals captured during qualitative interviews. Rather than relying solely on transcripts, multimodal analysis layers speech content with paralinguistic cues such as hesitation, pitch shifts, and emotional tone, alongside behavioral signals like facial expressions and on-screen objects. This approach is particularly valuable in consumer insights and CMI contexts where the gap between what participants say and what they feel can be significant. By synthesizing these data streams together, research teams surface patterns that transcript-only analysis routinely misses, producing findings that are both richer in depth and more credible to stakeholders.