How TrustGraph built enterprise-grade agentic AI with Qdrant

TrustGraph has transitioned agentic AI from impressive demos to robust enterprise solutions by building a platform centered around availability, determinism, and scalability, with Qdrant as a key component. Their architecture, which is containerized and modular, integrates Apache Pulsar for resilient streaming, uses graph-native semantics with RDF and SPARQL templates for precise knowledge retrieval, and employs Qdrant vector search for efficient similarity searches. TrustGraph's system extracts facts for knowledge graphs, allowing queries to leverage both semantic similarity and graph structure, enhancing traditional retrieval-augmented generation (RAG) approaches. This enables the retrieval of more nuanced insights, such as causal relationships, rather than mere keyword matches. By maintaining a resilient backbone with Pulsar and a sophisticated retrieval process, TrustGraph achieves determinism, resilience, scalability, and simplicity, meeting production-grade requirements and European data sovereignty standards, thus transforming agentic AI into critical enterprise infrastructure.