When a customer calls about a banking problem, staff have to listen to the recording, summarize the request, and route it to the right queue. Besides hearing the words, they need to judge whether the matter is urgent, whether the customer is clearly unhappy, and whether anything still needs follow-up. Listening to every call is slow, and vague wording forces repeated confirmation.

Unlocking the value of multimodal data—from PDFs and images to audio and video—often requires multiple stages of parsing, transformation, model computation, and joining results with related data. This article explains why these pipelines need a multimodal-native engine and how Vane Data brings SQL, Python functions, and AI capabilities together in a unified dataflow, making multimodal data processing as simple as data warehouse ETL. Start with a single line, import vane, and build your first multimodal processing pipeline using the Python and SQL you already know.

AstroVela has officially joined NVIDIA Inception.

When you first build a knowledge base, you may only need to import a few product manuals to see whether AI can answer business questions. Once the results prove useful, more content follows: historical reports, operating guides, internal policies, and more. A handful of PDFs gradually becomes thousands.

Merchants sometimes choose the wrong category for a product, and checking every title and image by hand takes time. This article uses a small batch of listings to demonstrate assisted review: a model checks whether the title, images, and category agree and flags listings for a person to review. Once a reviewer confirms a mistake, similarity search finds other listings in the batch worth checking.

More capable models lower the barrier to multimodal auto-annotation. Using sports video as an example, this article explores how Vane Data connects data preparation, self-hosted inference, external APIs, and result validation to produce more quality annotations within a fixed resource budget.

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.