Graph RAG
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 move AI from a curiosity to a core business tool, organizations must ground these models in their own proprietary, real-time data. This is the promise 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.
Graphwise Enterprise-Ready Workflow Engine
Most companies are currently stuck in the “Prototype Plateau”: they have built a basic RAG system in Python that works in controlled environments but fails in production because it is brittle, hard to debug, and inaccurate.
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.
Rapid Time-To-Value
Pre-loaded templates to deploy in days rather than quarters
Visual Debugging
High-control interface to trace execution paths and troubleshoot
Guardrails & Governance
Built-in filters to ensure safety, compliance, and factual consistency
Step by Step
Often referred to as “Advanced RAG” or “Semantic RAG” in the literature, our Graph RAG is a cascade of the following context-infused methods and LLM calls.
Step 1: Smart Query Builder
An assistant is already available when formulating the search. Auto complete and concept suggestions help to formulate the question in a targeted and domain-specific manner.
Step 2: Knowledge Retriever
The text mining implemented in the retriever extracts the semantic context of the query and provides the LLM with a list of directly identified and related concepts.
Step 3: Conversational Generation
The LLM processes the user query and context from a knowledge graph to produce an answer that is enriched with background information as summarized text. Depending on what the user is looking for, the dialog can be deepened or generalized by means of prompting the machine with additional questions – in other words, by having a conversation with the LLM.
Step 4: Document Recommender
A recommendation algorithm identifies documents of the company knowledge base that best match the result of the human-machine dialog and returns them as a list of summaries. This does not require sharing the knowledge base with the LLM provider, ensuring that company data remains secure.
Step 5: Conclusion
Our Advanced RAG delivers relevant and actionable results in each section. As a final step, we process these results with a final LLM stage to produce an easily understood conclusion.
Example applications
Conversational AI and Generative Search Experiences
The best choice if you want to harness the developments of Generative AI for your company. This option provides all the benefits explained above including recommendations and conversational generation.
Avalara Success Story
Avalara overcame the limitations of vector-based RAG by implementing a DOM GraphRAG proof of concept model. They used their existing DITA structured content to achieve 100% precision in content mapping. This established a foundation for reliable, mission-critical AI applications in tax and financial services.
Overcome the Limits of Large Language Models
The possibility of hallucinations with LLMs can never be ruled out. The forms that occur are sometimes easier or more difficult for people to recognize. We can limit the frequency of their occurrence with our Graph RAG methods.
Type of Hallucination
Nonsensical output. The LLM generates responses that lack logical coherence and comprehensibility.
Factual contradiction. This type of hallucination results in the generation of fictional and misleading content, yet still are presented as coherent despite their inaccuracy.
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.
Problems With Conventional LLM
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 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.
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.
PREMIUM
Mitigation With Semantic RAG
The Smart Query Builder injects the semantics of a word when formulating the query and thus unmistakably determines its meaning for the LLM.
The contextual and domain-specific knowledge provided in the Semantic RAG fills in data gaps and leads the LLM to meaningful answers.
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 of Large Language Models
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.
Our combination of AI assistants ensures that usable knowledge is available as soon as the query is entered. Instead of long lists of documents to be processed, our AI solution delivers summarized facts. We are therefore talking about an increase in efficiency of 15-20% through the use of Graph RAGs in everyday operations.
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.
Our AI-guided search assistant now helps inexperienced users to formulate search queries correctly. The LLM-supported human-machine dialog picks up employees at their current level of knowledge and enables on-the-fly exploration and learning.
And faster onboarding to topics and jobs means faster and greater productivity for your employees.
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. There are currently four known optimization methods: Complete fine tuning, Parameter Efficient Fine Tuning (PEFT), Prompt Engineering and RAG.
Fine tuning, even efficient fine tuning, requires a significant amount of computing power, time and ML expertise that you need to invest regularly to integrate new relevant data into the model. That’s why we rely on a combination of Prompt Engineering and RAG, both of which are independent of costly LLM customization and limited in cost-saving maintenance of knowledge base and graphs.
With our Graph RAG, we can cut down costs of LLM implementation and maintenance by 70%. This creates an ROI increase of 3x and higher.
Costs in %
Want to see Graph RAG in your own enviroment? Check out our Generative AI Starter Kit!
Useful Resources
Responsible AI based on LLMs
Document Object Model Graph RAG
Conversational AI for the Workplace
This eBook dives into the knowledge-hub.eco, a PoolParty demo application that’s based in Graph RAG to give you advanced Conversational AI for the Workplace.
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
Get more insights in the white paper
This white paper aims to provide seven cases for knowledge graphs in RAG architecture that not only remedy the issues encountered but provide additional benefits on top.