Multimodal models handle documents, images and audio as native inputs, which removes whole pipelines. They also fail in ways text models do not, and those failures are less visible because the input is harder for a reviewer to skim.
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Images and audio consume far more tokens than the equivalent text, and a high-resolution page can cost more than several pages of extracted text would. For high-volume document processing, that difference frequently decides the architecture: a cheap classifier routing to full multimodal processing only where layout genuinely matters, rather than sending everything through the expensive path.
It also strengthens the case for self-hosting at volume. Document and image workloads are where per-token pricing bites hardest, and where owning the hardware changes what is economically worth processing.
Sometimes, and it is worth testing rather than assuming in either direction. Multimodal models handle layout and context better than traditional OCR; dedicated OCR is often more accurate on dense text and dramatically cheaper at volume. A hybrid — OCR for text, multimodal for layout and structure — is frequently the right answer.
Build an evaluation set from your real documents with the correct extractions labelled, including the awkward ones — poor scans, unusual layouts, handwriting. Aggregate accuracy on clean documents tells you very little about production, where the awkward ones are the whole problem.
Yes. Open-weight vision-language models are capable and improving, and for document workloads involving confidential material — which is most of them — that is usually the right deployment. Memory requirements are higher than text-only models of comparable size, so size accordingly.
Aggregate accuracy on clean documents predicts almost nothing about production.
30-minute technical call · no deck
What we built, what stage it is at, and a link to the live thing wherever there is one.
yourOS is an operating system with an AI at its heart. You talk to it like a person, out loud or typed. It makes your calls, plans your days and works on your projects with a crew of agents, on your own private server. Our own product, built from the first commit; the waitlist is open.
Website, backend and app assets for Sonora, a free AI sound-therapy app on iOS and Android published by Cognitive AI.
Brand, website, prototype, apps on five platforms, the AI backend and the algorithm-intelligence pipeline for Podswap, a social growth engine that runs without a human in the loop.
In closed beta: Roxy, a private AI assistant with permanent memory running on infrastructure the user owns. Brand, site, product, backend and conversational design.
Live at coherence.com: brand identity generated with AI agents we directed, the motion system and website for the group behind Sonora, AboutAI, WakeUp and MudShadows. Agent work remains in build.
Live at domainbooth.com for DomainBooth, a premium domain brokerage: a new animated mark, every page redesigned and rebuilt with designed mobile layouts, and a custom CMS for its sales record, featured names, press and enquiries.
Apps, platforms and campaigns for these teams, long before the AI work. Every one is written up on the work page — what we did, and where our part ended.
