Images are increasingly used in search and AI answers, but most systems still reduce them to filenames, alt text, or loosely generated captions. That makes it hard to understand what’s actually in an image, why it’s relevant, and how it should be used in retrieval.

VisualQueryPro analyzes images and extracts structured, inspectable visual signals that can be reused in modern search and AI-driven retrieval workflows. The focus isn’t classic image search, but turning visual content into explicit signals that support context, trust, and attribution.

The project is still in alpha. Payments aren’t enabled yet, but we’re offering free credits on request to gather feedback and iterate on the approach.

Happy to answer questions about the analysis pipeline, structure vs. embeddings, or how visual signals fit into existing SEO and search systems.