Polyvia

Polyvia is a visual knowledge index purpose-built for AI agents and MCPs (Model Context Protocols), turning scattered visuals across thousands of documents into a single, queryable source of truth. As a multimodal document retrieval and reasoning platform, it serves two primary audiences: developers building AI agents that need to retrieve from complex visual data, and enterprise knowledge workers who must extract insights from large volumes of reports, decks, and statements. Its core value proposition is enabling fast, accurate, cited retrieval over 100K+ multimodal files—including PDFs, PPTX, images, and audio—without the complexity of stitching together multiple tools. By indexing visual information at scale and connecting facts across the corpus, Polyvia unlocks insights that would otherwise remain trapped in disparate documents.

Traditional approaches to multimodal document retrieval face a critical bottleneck: file-by-file search using agentic tools like Claude Code or Cowork becomes impractically slow beyond roughly 100 files. At scale, users are forced to assemble a stack of vendors—Reducto for extraction, LlamaIndex for indexing, and custom retrieval logic—each with its own integration overhead and failure modes. This fragmented workflow introduces complexity, latency, and errors. Polyvia solves this by providing an end-to-end solution that eliminates the need for separate extractors, PDF parsers, or manual stitching. The concrete pain point is the inability to query across thousands of visual documents with sub-second latency while maintaining citation accuracy, which directly impacts productivity in time-sensitive tasks like due diligence and financial analysis.

The first major feature is the VLM Visual Extractor, a state-of-the-art visual document extractor and parser. This fine-tuned VLM-OCR pipeline is specifically designed for the hardest visual inputs—including complex charts, dense tables, scanned invoices, and handwritten notes. Unlike generic OCR tools that produce 300-token descriptions, the VLM Visual Extractor extracts actual data points, preserving numerical values, row-column relationships, and text structure. It works by combining a vision-language model with specialized OCR fine-tuning to interpret visual layouts accurately. This feature is useful because it ensures that downstream retrieval operates on precise, structured data rather than noisy or superficial captions, enabling reliable cross-document comparison and quantitative analysis.

The second major feature is the Multimodal Knowledge Ontology, a knowledge graph designed for large-scale visual file search. This component disambiguates extracted facts into unique entities—for example, distinguishing EBITDA from different quarters or companies—and connects related facts across the entire corpus. By linking information across thousands of documents, it creates a single source of truth for cross-document reasoning. The ontology supports entity linking at scale, handling over 100,000 files while maintaining query consistency. This is useful because users can ask complex comparative questions—like 'Compare EBITDA across all filings'—and receive answers that aggregate facts from multiple sources, with each fact traceable back to its original document and location.