Polyvia – Visual Knowledge Index for querying and reasoning over visual data

Summary: Polyvia indexes and reasons over visual data from charts, tables, and infographics, creating a queryable knowledge graph that disambiguates facts across tens of thousands of documents. It enables multimodal agents and knowledge teams to extract precise information from visual sources where text-based tools fail.

What it does

Polyvia uses VLM-OCR to extract structured data from visuals and builds a facts-ontology graph to connect and disambiguate information. It supports agentic visual reasoning with audit-ready citations, allowing queries that retrieve detailed answers from visual content.

Who it's for

It is designed for developers of multimodal agents and knowledge-work teams in fields like research, finance, and healthcare who need to access and reason over large collections of visual documents.

Why it matters

Polyvia addresses the gap where most enterprise knowledge is visual and current AI tools cannot reason across large visual datasets, reducing hallucinations and improving search accuracy.