Multimodal AI

A vision-language model reads images as visual tokens beside your text, so one network can answer questions about what is in a picture.

OCR copies the letters off a page; a VLM can explain the page — for invoices and IDs you extract structure, then let the model reason.

Cascades transcribe speech to text, think, then speak back — native audio models keep tone but are harder to search; most products still need both.

Video is a timeline — sample frames, index audio and on-screen text, retrieve the seconds that matter; don't pour the whole file into the context window.

Generators invent pixels from a prompt — they are not cameras. Photorealism is not proof.

Multimodal RAG retrieves images, frames, OCR, and audio — keep native media when pixels or sound are the answer.

A multimodal agent uses the same observe-reason-act loop — screenshots and mics are observations, with extra injection risk.