AI applications often need to process PDFs, images, audio, and video. Before they enter a data pipeline, PDFs must be split into pages, images decoded, audio resampled, and video expanded along its timeline, while filenames, page numbers, and frame indices remain attached as location metadata. Using these four file types, this article shows how Vane Data centers the workflow on a Relation—a multimodal dataset—to connect file expansion, batch processing, model inference, queries, and writes, turning raw files into queryable data.

Vane Data is a multimodal-native data engine built on DuckDB that brings data processing, model inference, and heterogeneous resource scheduling into a single Relation pipeline. This article explains how it combines AI Functions, Python UDFs, and vLLM; uses dynamic batching, pipeline parallelism, backpressure, and fault tolerance to run workloads reliably; and applies them to an auto-insurance workflow from image preprocessing to claims review routing.