GraphRAG
The Trust Layer for Enterprise AI: Production-Ready GraphRAG
While LLMs offer a massive competitive advantage, they are inherently domain-agnostic and “frozen” in their training state. For enterprises, this creates a dangerous “Hallucination Gap” where the AI generates confident but factually incorrect or nonsensical answers.
To turn AI into a reliable business tool, you must ground it in your proprietary, real-time data. This is the goal of Retrieval-Augmented Generation (RAG).
What is RAG?
Retrieval Augmented Generation (RAG) is a framework designed to make LLMs more reliable by providing them with relevant, up-to-date knowledge from a company’s documents. This context is then fed to the LLM alongside the user’s question, ensuring the response is based on specific facts rather than generic internet data.
However, conventional RAG has a significant limitation: it treats your data as a flat list of text chunks. It can find similar words, but it cannot understand the complex relationships, hierarchies, or logic that connect your business information.
What is GraphRAG?
GraphRAG (Graph-enhanced RAG) is the evolution of AI retrieval. It replaces the flat, “vector-only” approach with an an advanced architecture that uses a Knowledge Graph to provide a “context-infused” retrieval layer.
By mapping your data into a network of entities and relationships, GraphRAG allows the LLM to navigate your enterprise knowledge like an expert, not a keyword search engine.
Relational Intelligence
Instead of treating data as isolated “chunks,” GraphRAG understands hierarchies and dependencies, following your business logic
Overcome the Limits of Large Language Models
The possibility of hallucinations with LLMs can never be ruled out, but we can significantly limit their frequency by replacing “guessing” with “Graph-based grounding.”
| Type of Hallucination | Problems With Conventional LLM | Mitigation With Semantic RAG |
|---|---|---|
|
Nonsensical output. The LLM generates responses that lack logical coherence and comprehensibility. |
LLMs sometimes have problems with understanding context. They may not be able to distinguish between different meanings of a word and use it in the wrong context. The higher the ambiguity of a query, the higher the probability of leading the LLM down the wrong path. |
The Smart Query Builder injects the semantics of a word when formulating the query and thus unmistakably determines its meaning for the LLM. |
|
Factual contradiction. This type of hallucination results in the generation of fictional and misleading content, yet still are presented as coherent despite their inaccuracy. |
The data with which the LLM was originally trained is not relevant in terms of time or context to solve the question posed. The LLM begins to fill in the data gaps with hallucinations. |
The contextual and domain-specific knowledge provided in the Semantic RAG fills in data gaps and leads the LLM to meaningful answers. |
|
Prompt contradiction. The LLM generates a response that contradicts the prompt used to generate it, raising concerns about reliability and adherence to the intended meaning or context. |
LLMs have their own rules, policies and strategies set by their parent company. They prevent them from distributing unwanted content, even if it is contained in the training data. If the LLM detects a violation of these rules, possible responses are decoupled from the request. |
The Smart Query Builder guides the formulation of the prompt and can take the rules of the LLM into account in advance. Of course, changing the LLM provider or fine-tuning can also shift the rules. |
Unlocking the Business Potential: KPIs and ROI
Across all industries, there is a consensus that the use of LLM can increase productivity in almost all areas of a company. According to a study by Deloitte, 82% of managers believe that AI will improve the performance of their employees. Gartner predicts that companies will save at least 20% by using Generative AI in the coming years.
Shorten the Time to Insight
According to IDC, a knowledge worker spends around 30 % of their working day searching for information – primarily reviewing search results and processing them. Instead of long lists of documents, our GraphRAG solution delivers summarized facts.
Outcome: An increase in operational efficiency of 15-20%.
Savvy querying for the untrained
That’s a dilemma! Companies want to familiarize their employees with a topic quickly. However, in order to make successful search queries, domain knowledge (jargon and terminology) of a subject area is required.
Outcome: Our AI-guided search assistant helps inexperienced users formulate queries correctly, enabling on-the-fly exploration and faster onboarding.
Low-cost for Implementation and Maintenance
It is common knowledge that even the best pretrained LLMs might not always meet your specific needs. You need to customize the model in terms of expertise, vocabulary and timeliness. To adapt it to your specific requirements, you need to optimize it. Fine-tuning models is costly and requires significant computing power.
Outcome: By combining Prompt Engineering with GraphRAG, we cut implementation and maintenance costs by 70%, creating an ROI increase of 3x or higher.
Graphwise Enterprise-Ready Workflow Engine
Most companies struggle to move RAG solutions from development to production because current systems are often “black boxes” built with complex, code-heavy frameworks. While many developers can build a successful prototype in an experimental setting, these solutions often fail to scale because they lack the governance and observability required by modern business.
Graphwise GraphRAG is the first production-ready “Trust Layer” designed to turn these prototypes into enterprise-grade systems with zero friction.
Graphwise democratizes the creation of advanced AI systems by empowering the entire technical team, and not just senior developers, to build, debug and ship AI workflows. Graphwise GraphRAG bridges the gap between complex enterprise data and reliable, trustworthy AI agents through a low-code, visual workflow engine.
Example applications
Conversational AI and Generative Search Experiences
Avalara Success Story
Step by Step
Our GraphRAG architecture uses a cascade of context-infused methods to ensure maximum precision:
By 2027, more than 40% of digital workplace operational activities will be performed using management tools that are enhanced by GenAI, dramatically reducing the labor required.
Predicts for Generative AI
Cameron Haight, Chris Matchett, 2024
Useful Resources
Graphwise GraphRAG
Scale your enterprise intelligence with a GraphRAG solution from Graphwise. Move from concept to production in days, not months.
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What Is GraphRAG?
Retrieval Augmented Generation enhances LLMs with external knowledge for more accurate, contextual question answering.
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Graphwise GraphRAG
Demonstrates how a connected system, powered by semantic search through GraphRAG, helps diverse stakeholders find accurate answers faster and reuse knowledge consistently.
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The Semantic Advantage: Scaling Enterprise-Ready GraphRAG and Trustworthy AI with Graphwise
Download this white paper to discover how Graphwise platform offers an enterprise-ready, low-code engine to operationalize explainable, governed and context-aware AI for the modern enterprise.
Read moreGet more insights in the white paper
Download this white paper to discover how Graphwise platform offers an enterprise-ready, low-code engine to operationalize explainable, governed and context-aware AI for the modern enterprise.