Graphwise has launched GraphRAG, a low-code AI-workflow engine that instantly transforms Python prototypes into production-grade systems. Built on a trusted semantic layer using knowledge graphs, GraphRAG unites LLMs, enterprise data, structured knowledge, and multiple search methods to deliver precise, verifiable, hallucination-reduced answers—outperforming standard RAG by preserving complex relationships and grounding responses in enterprise facts.
Quick Intel
Overcoming Limitations of Standard RAG Traditional RAG systems often chunk and flatten data, losing critical relationships and leading to hallucinations, shallow retrieval, and answer drift. GraphRAG addresses these by leveraging a fully-fledged knowledge graph as the semantic backbone, ensuring AI responses remain grounded in verifiable facts and complex interconnections. This approach transforms enterprise data into a reliable foundation for intelligent agents and applications.
Benchmark-Proven Superiority Graphwise demonstrated that enhancing HippoRAG—one of the leading GraphRAG implementations—with an ontology-based knowledge graph significantly outperforms the schemaless version on the MuSiQue benchmark. MuSiQue tests advanced multi-hop reasoning over simple fact retrieval, highlighting GraphRAG's ability to handle sophisticated queries with greater accuracy. Independent analyst Alan Morrison noted this as a clear advancement in GraphRAG benchmarking, urging customers to prioritize ontology-driven solutions for higher precision.
"The MuSiQue dataset is a clear step forward toward better GraphRAG benchmarking," said Alan Morrison, Independent Graph Technology Analyst and author of The GraphRAG Curator. "The test proved that Graphwise's approach for semantic GraphRAG consistently outperforms one of the best GraphRAG systems, which uses a schemaless associative graph. While most of the GraphRAG offerings on the market today use the same schemaless approach, customers should be demanding the level of accuracy that comes with ontologies and fully-fledged use of graph databases."
Key Features Enabling Rapid, Trusted Deployment GraphRAG's low-code visual engine allows subject matter experts to customize AI logic without deep coding expertise. Pre-built templates accelerate time-to-value, enabling deployment of specialized agents like Policy Q&A or Technical Support in days. A semantic metadata control plane elevates accuracy from around 60% to over 90% by grounding responses in enterprise truth, while explainability panels and provenance tracking support regulatory needs in sectors such as finance and pharmaceuticals. Visual debugging reduces troubleshooting time by up to 80%, and SKOS-style enrichment ensures AI interprets company-specific terminology accurately.
"Enterprises are increasingly tired of brittle RAG pipelines that result in shallow retrieval, answer drift, disappearing business logic, and knowledge trapped in silos," said Andreas Blumauer, SVP Growth at Graphwise. "Because GraphRAG is based on a solid knowledge graph foundation, it removes traditional obstacles by transforming data into a trusted semantic backbone. New no-code capabilities make it easy to deploy intelligent agent-based systems and powerful AI applications to automate knowledge quickly and easily so organizations can make generative AI reliable and scalable for businesses."
Bridging Prototypes to Production at Scale GraphRAG bridges the common gap where AI prototypes stall in development by providing a production-ready engine that grounds agents in enterprise-grade knowledge graphs. This empowers organizations to move beyond experimentation toward reliable, scalable generative AI applications with reduced risk and faster ROI.
Graphwise invites interested organizations to join a complimentary webinar on February 18th from 10:00 a.m. – 11:00 a.m. EST or download the comprehensive whitepaper for deeper insights into GraphRAG's capabilities and implementation.
About Graphwise
Graphwise, enables organizations to unlock ROI for enterprise AI by delivering the most comprehensive and trusted industry solution in the field of knowledge graphs and semantic AI technologies. As enterprises pour millions into AI investment, Graphwise delivers the critical knowledge graph infrastructure that ensures that enterprises can realize the technology's full potential, is trusted, and can be implemented at scale. Graphwise, which is the result of the recent merger of Ontotext with Semantic Web Company, has over 200 employees worldwide, with offices located across North America, Europe, and APAC.
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