Multimodal AI: Beyond Text-Only Assistants

Multimodal models accept images, audio, or documents alongside text, opening use cases like analysing a photo of damaged goods or transcribing a support call automatically. For retail and logistics clients, this often means processing a product photo and description together rather than needing separate systems for each.

Multimodal models can accept images, audio or documents alongside text, which opens up use cases like analysing a photo of damaged goods or transcribing and summarising a support call automatically.

For clients in retail and logistics, this often means an AI system that can process a product photo and a text description together, rather than needing separate systems for each.

We are increasingly building multimodal features where the added accuracy is worth the additional cost, while keeping simpler text-only prompts for lower-stakes tasks.

A concrete example: a logistics client processing damage claims can feed a photo of a damaged package alongside the customer's written description into a single multimodal request, getting a more accurate assessment than either the image or the text could produce alone.

A common pitfall is defaulting to multimodal processing for every request regardless of whether an image or audio file is actually relevant, which adds unnecessary cost and latency to tasks a simple text prompt would have handled just as well.

Audio transcription in particular benefits from combining automatic transcription with a language model pass afterward, since raw transcripts often contain errors that a model can catch and correct using surrounding context.

We scope multimodal features around the specific case where combining inputs demonstrably improves accuracy over separate systems, rather than adopting multimodal processing as a default simply because the underlying models now support it.