How TrustGraph built enterprise-grade agentic AI with Qdrant - Qdrant

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This post details TrustGraph's technical implementation of enterprise-grade agentic AI using Qdrant. It highlights their architecture which combines Apache Pulsar for resilient streaming, RDF for graph-native knowledge modeling, and Qdrant for vector search. Key technical aspects include: extracting facts and embedding entities for dual representation in Qdrant and the knowledge graph; a query process that embeds queries, retrieves nearest entities from Qdrant, expands to a subgraph, and feeds this curated context to an LLM; and an agentic AI framework that orchestrates GraphRAG, template-driven queries, and tool invocation. The post emphasizes production-grade outcomes like determinism, resilience, scalability, and data sovereignty achieved through this integration.