Polyvia is a Visual Knowledge Index for Agents & MCPs that turns scattered visuals into a queryable source of truth. It indexes and reasons over visuals, connecting facts across thousands of documents.
Polyvia is a visual knowledge index for agents and MCPs, a category-defining multimodal document retrieval platform designed to turn scattered visuals—charts, tables, scans, invoices, handwriting, and audio—into a queryable source of truth. By indexing and reasoning over visuals, it enables cross-document reasoning across up to 100,000 files with sub-200ms latency, serving both developers building AI agents and enterprise knowledge workers conducting research or due diligence. Its core value is eliminating the need to stitch together multiple vendors (like Reducto and LlamaIndex) for extraction, ontology building, and retrieval, providing an end-to-end solution that extracts actual data points rather than 300-token descriptions.
The primary pain point it addresses is the inefficiency of file-by-file agentic search, which becomes unusably slow past about 100 multimodal documents. Traditional retrieval systems struggle with complex visuals—charts, tables, scanned documents, and audio—requiring separate extractors and parsers that introduce latency and complexity. Polyvia solves this by offering a unified pipeline that ingests, extracts, links, and retrieves information at scale, cutting query time from minutes to under 200 milliseconds. This matters because knowledge workers and AI agents need to analyze large volumes of diverse documents quickly and accurately, without manually parsing each file.
The first major feature is the VLM Visual Extractor, a fine-tuned VLM-OCR pipeline trained for the hardest visual inputs. Unlike generic OCR that produces simple text, this extractor extracts actual data points—such as revenue figures from charts, line items from invoices, or damage descriptions from claim photos—rather than generic 300-token descriptions. It handles charts, complex tables, scans, invoices, and handwriting with high fidelity. This is crucial because it ensures downstream retrieval operates on precise, structured facts, enabling accurate cross-document comparisons and reasoning without manual data entry.
The second feature is the Multimodal Knowledge Ontology, a knowledge graph that disambiguates extracted facts into unique entities and connects them across the entire corpus. For example, if the same company name appears in multiple filings, the ontology links those mentions, creating a single source of truth. This enables queries like 'Compare EBITDA across all filings' to yield correct answers even when the metric is expressed differently across documents. It supports cross-document reasoning across 100K+ files, turning a collection of disparate documents into an interconnected knowledge base. This is essential for due diligence, credit monitoring, and research tasks that span numerous sources.
Publisher
A
admin
Launch Date2026-02-02
Platformweb
Pricingpaid
Domain Authority
Ahrefs DR1/100