Verbis graph - graphrag knowledge retrieval engine

Standard RAG returns semantically similar passages. Neither connects the dots between entities across your corpus VERBIS GRAPH solves this by organizing documents into a unified graph-based knowledge layer that maps relationships between entities - enabling accurate retrieval, contextual understanding, and multi-hop reasoning

HOW IT WORKS By combining semantic vector search with knowledge graph traversal, VERBIS GRAPH delivers context-aware retrieval across large document collections. When you ask a question that spans multiple documents or entities, VERBIS GRAPH traverses the relationships between them -returning a synthesized, grounded answer rather than a list of passages

Full Answer TRACEABILITY Every response includes references to the exact document passages and sources used to construct the answer. This transparency enables result verification, builds trust in AI outputs, and supports compliance-sensitive environments where explainable AI is essential for audit and governance requirements

BUILT FOR

• LEGAL/compliance teams requiring auditable AI outputs
• FINANCIAL analysts and capital markets teams working across multi-document research, earnings reports, and cross-entity data analysis
• HEALTHCARE professionals extracting insights from clinical documentation, patient records, and medical literature
• PHARMA / LIFE SCIENCES organizations accelerating drug research, clinical trial analysis, and regulatory submission workflows
• ENTERPRISE replacing manual search workflows with AI-powered, citation-grounded doc intelligence