Papr GraphRetrieve answers by correctness, not just similarity

Papr Graph

Papr Graph transforms how AI systems understand documents by adding relational intelligence to vector embeddings. Unlike traditional cosine similarity that only finds textually similar content, Papr Graph reranks results based on real-world context—version history, approval status, entity relationships, and temporal relevance—ensuring users get the correct answer, not just the most similar-sounding one.

Product Highlights

• Graph-Aware Reranking: Attaches 14+ relational dimensions to every document vector, including entities, relations, time, and ownership
• Intent-Based Matching: Rotates embedding space at query time to surface documents that actually answer the question
• Zero-Retention Security: Stateless architecture processes data in-memory with no storage, logging, or persistence of your embeddings or queries
• Drop-In Integration: Works with any embedding model (OpenAI, Cohere, Voyage) and existing vector databases via two simple API calls
• Custom Schema Support: Define your own graph signals for domain-specific ranking logic

Use Cases

• Enterprise Knowledge Retrieval: Prevent outdated policy versions and unapproved drafts from ranking above current, authoritative documents
• Legal & Compliance Search: Ensure regulated content surfaces based on jurisdiction, approval status, and effective dates
• Multi-Entity Organizations: Route queries to correct subsidiaries, departments, or product lines based on ownership relationships
• Version-Controlled Documentation: Automatically deprioritize archived or superseded documents regardless of textual similarity

Target Audience

Papr Graph serves engineering teams and product managers building retrieval-augmented generation (RAG) systems for enterprises with complex document hierarchies, approval workflows, or multi-entity structures—particularly in regulated industries like legal, financial services, healthcare, and government.