Multi-modal RAG

Also known as: multi-modal RAG, multimodal RAG, multimodal retrieval

Retrieval-augmented generation that grounds answers in more than text — pulling from video, images, and signals as well as documents.

Multi-modal RAG extends retrieval beyond text, so a model can ground its reasoning in video, imagery, and other signals alongside documents. At North AI this meant indexing film trailers and visual content so the system could reason about what an audience actually sees, not just a written synopsis.

The promise is that meaning often lives outside text — a trailer’s pacing, a face, a cut. Bringing those into the same retrieval space as words is where a lot of the real-world value, and the real engineering difficulty, sits.