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A visual knowledge index for agents and MCP workflows that turns document-heavy visual data into a queryable source of truth.
Polyvia is a multimodal retrieval API and infrastructure layer that organizes scattered enterprise visual data into a queryable knowledge graph for AI agents. It targets the fact that a large share of business knowledge lives inside charts, slides, tables, and financial filings that standard chat-style ingestion struggles to parse accurately or at scale.
The platform handles structured extraction from documents (PDF, DOCX, MD, TXT), slides (PPTX), images (PNG, JPG, WEBP), and audio (WAV, MP3, M4A), then connects extracted facts by entity, time period, source, and metric type so cross-document queries return in sub-50ms. Every answer ships with audit-ready citations down to page, section, bounding box, or audio timestamp.
Developers can integrate via Python and TypeScript SDKs, a REST API, an MCP server, Agent Skills, or a no-code studio UI. Enterprise options include on-prem and VPC deployment, SOC2, and bring-your-own-LLM. Common use cases shown on the page include financial analysis across 10-Ks and investor decks, and building agents that reason over large libraries of internal reports.
