Polyvia is the first Visual Knowledge Index designed specifically for Agents and MCPs (Model Context Protocols). It transforms scattered visual data from documents into a unified, queryable knowledge source where every fact is disambiguated and connected. Unlike other tools that either extract visuals or index text separately, Polyvia uniquely indexes and performs reasoning directly over visual content.

The core capabilities include creating one connected knowledge graph of facts with every fact disambiguated by company, quarter, source, and page. It features VLM-OCR extraction that converts charts, tables, and diagrams into structured facts through visual logic extraction from any document type. The platform provides visual citations that trace every answer back to its source document, page, and section for audit readiness. It enables cross-document agentic reasoning by connecting facts across thousands of documents rather than leaving them scattered.

Polyvia works by building a visual AI infrastructure layer that supports visual search and reasoning at scale on a graph of connected facts. It processes visual data through advanced extraction techniques to create structured, queryable knowledge that maintains source attribution throughout.

The benefits include creating a single source of truth for visual knowledge, enabling audit-ready citations for all answers, and supporting querying across massive document sets. Use cases include knowledge-work teams performing visual search and exploration across millions of charts and slides, and developers building multimodal agents that require visual reasoning capabilities.

The target users are developers building multimodal agents and knowledge-work teams requiring sophisticated visual data processing. It offers enterprise-ready deployments with integrations including S3, Snowflake, SharePoint, CRM, and ERP systems, supporting private deployments and BYOK (bring your own LLM) policies.