Multimodal Input Pipelines: Documents, Images, Audio, Video
Real products eat messy inputs: scanned PDFs, photos, meeting recordings, video. This topic is about turning them into model-ready context — the vision/OCR/ASR/video stages, the tokenization and cost math behind each choice, and the durable two-stage pattern: normalize cheaply first, then spend expensive reasoning.
01.The Problem: Your Users Do Not Type Clean Text
You want to build something simple-sounding:
"Ask questions about our 5 million scanned contracts."
"Summarize yesterday's hour-long sales meeting."
"Read this photo of a receipt and file the expense."
The model underneath can only consume tokens — numbers cut from text, or from image patches and audio encodings. It cannot nibble on a PDF file, an MP3, or an MP4 directly, at least not cheaply or reliably.
So between "messy real-world file" and "model can reason about it" sits plumbing:
- a scanned page must become text (or be seen as an image),
- speech must become a transcript (with who-spoke-when attached),
- video must become a handful of informative frames,
- everything must fit inside a token budget that is also a dollar budget.
That plumbing is the multimodal input pipeline. In 2026 it is less about "can the model see?" (mostly, yes) and more about a quieter question:
Which hop — raw pixels/audio, or a cheap pre-transform into text — is cheapest AND most reliable for my data?
A production multimodal ingestion pipeline
A production multimodal ingestion pipeline
Cheap specialized transforms upstream, one expensive multimodal reasoning stage downstream, schema-validated output.
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