New GraphRAG Solution Moves Beyond Vector-Only RAG – Knowledge Graphs Provide Context and Common Sense to AI
•February 16, 2026
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Why It Matters
By delivering verifiable, context‑rich answers, GraphRAG reduces operational risk and compliance exposure for enterprises adopting generative AI. Its low‑code approach speeds time‑to‑value, making trustworthy AI accessible to non‑technical business units.
Key Takeaways
- •GraphRAG reduces hallucinations using ontologies
- •Accuracy improves from 60% to over 90%
- •MuSiQue benchmark errors cut more than half
- •Low‑code engine enables production AI in days
- •Explainability panels meet regulatory compliance
Pulse Analysis
Traditional retrieval‑augmented generation (RAG) pipelines rely on flat vector stores that fragment relational context, leading to hallucinations and shallow answers. Graphwise’s GraphRAG replaces that approach with a knowledge‑graph‑backed semantic layer, preserving entity relationships and enabling multi‑hop reasoning. By integrating ontologies directly into the retrieval process, the engine supplies LLMs with structured, verifiable facts rather than isolated text chunks, which restores common‑sense reasoning and reduces answer drift. This architectural shift marks a maturation point for enterprise generative AI, where accuracy outweighs raw speed.
The company’s internal tests on the MuSiQue benchmark—a rigorous multihop question set—showed more than a two‑fold drop in incorrect responses compared with leading schemaless GraphRAG solutions. Coupled with a reported jump in answer correctness from roughly 60 % to over 90 %, the results translate into tangible risk mitigation for regulated sectors such as finance and pharma. Explainability panels and provenance tracking further satisfy audit requirements, while visual debugging cuts troubleshooting time by up to 80 %. In practice, enterprises can move from prototype to production in days rather than months.
GraphRAG’s low‑code visual engine democratizes AI development, allowing subject‑matter experts to configure agents without deep Python expertise. Out‑of‑the‑box templates accelerate deployment of use cases like policy Q&A or technical support, delivering immediate ROI. As organizations grapple with siloed data and mounting compliance pressures, a graph‑centric RAG solution offers a scalable path to trustworthy generative AI. Analysts predict that vendors that embed ontologies will capture a growing share of the AI‑ops market, while customers increasingly demand the transparency and accuracy that GraphRAG promises.
New GraphRAG Solution Moves Beyond Vector-only RAG – Knowledge Graphs Provide Context and Common Sense to AI
Latest Graphwise offering bridges the gap between complex enterprise data and functional AI agents, using ontologies reduces inaccurate answers 2X in benchmarks
Graphwise, the leading Graph AI provider, announced the immediate availability of GraphRAG, a low-code AI-workflow engine designed to turn “Python prototypes” into production-grade systems instantly. Graphwise GraphRAG is based on a trusted semantic layer that reduces hallucinations and delivers precise and verifiable answers. GraphRAG unites LLMs, enterprise data, structured knowledge, and multiple search methods to deliver transparent, verifiable, enterprise-ready answers. Unlike standard RAG that “flattens” data into chunks leading to lost relationships and hallucinations, GraphRAG treats the knowledge graph as a trusted semantic backbone, ensuring AI responses are grounded in verifiable enterprise facts and complex relationships.
Equally important, the company demonstrated that augmenting HippoRAG, one of the best GraphRAG systems, with an ontology-based knowledge graph reduces more than twice the inaccurate answers on the renowned MuSiQue benchmark. Considered the most advanced benchmark of its kind, MuSiQue (Multihop Questions via Single-hop Question Composition) is a challenging dataset designed to evaluate RAG-systems on complex, multi-hop reasoning tasks rather than simple fact retrieval. To learn more, click here.
“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.”
Graphwise bridges the gap between complex enterprise data and functional AI agents: While standard AI prototypes often stall in development, GraphRAG provides a production-ready, low-code engine that grounds AI agents in enterprise-grade knowledge graphs.
Also Read: AiThority Interview With Arun Subramaniyan, Founder & CEO, Articul8 AI
Features include:
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Low-Code Visual Engine democratizes AI, enabling subject matter experts to adjust AI logic visually without requiring Python developer involvement.
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Out-of-the-Box Templates provide guardrails and support query expansion that deliver the fastest time-to-value. Allows users to skip years of R&D by deploying a Policy Q&A and/or Technical Support agent in days instead of months.
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Semantic Metadata Control Plane eliminates hallucinations and moves AI accuracy from 60% to 90%+. AI responses are grounded in an organization’s “enterprise truth,” reducing legal and operational risk.
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Explainability and Provenance Panels support regulatory compliance. Built-in traceability affords transparency into how an AI response was produced, which is highly important in regulated industries such as pharmaceutical and/or finance.
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Visual Debugging and Monitoring reduce maintenance costs by eliminating black box code. If an agent fails, tech leads can visually trace the error path, cutting troubleshooting time by 80%.
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SKOS-style Concept Enrichment harnesses domain-specific intelligence. This means AI understands company specific jargon, acronyms, and synonyms out-of-the-box, so users get the right info regardless of how they ask.
“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.”
Also Read: Cheap and Fast: The Strategy of LLM Cascading (Frugal GPT)
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