Traditional chat assistants only process typed text. A multimodal assistant can also take in an image, an audio clip, or a document, and reason across all of it together: reading the text of a complaint while also looking at the photo of the defective part it references.

In enterprise operations, this shows up as an agent that can read a scanned invoice, interpret a handwritten note attached to a purchase order, or listen to a voicemail and extract the request, rather than needing every input pre-converted to clean text first.

Frequently Asked Questions

What counts as a "modality"?

Text, images, audio, video, and structured documents (PDFs, scanned forms) are the common ones in business contexts. A multimodal assistant handles at least two of these in the same interaction.

Why does multimodal support matter for enterprise workflows?

Real business documents are rarely clean text. Invoices are scanned, complaints come with photos, compliance forms are handwritten. A multimodal assistant reads the input as it actually arrives instead of requiring a manual conversion step first.

Is this the same as OCR (optical character recognition)?

OCR extracts text from an image. A multimodal assistant can do that and reason about the image itself, layout, handwriting, visual context, not just the characters on the page.

Does multimodal input change how accurate the assistant is?

It can improve accuracy by giving the system more context (a photo confirms what a vague text description means), but each added modality is also another place errors can enter. Zamp addresses this by verifying extracted data against source systems before acting on it, regardless of which modality it came from.