Graph-based RAG for enterprise with Writer Knowledge Graph
Starter guide
Introduction
This guide explores the practical applications of graph-based RAG and the ease of integration into existing systems using Writer Knowledge Graph. By the end, you’ll understand why graph-based RAG is the future of enterprise information retrieval and how Writer Knowledge Graph can drive significant value for your organization.
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Graph-based RAG is the key to scaling enterprise AI from pilot to production
Despite high hopes and heavy investment, enterprises have entered the trough of disillusionment regarding generative AI. A global survey of 500 leaders at enterprise companies found that only 17% rate their in-house AI projects as excellent. Additionally, McKinsey reports that only 11% of companies have adopted generative AI at scale. In short, CIOs and CAIOs are struggling to scale initiatives beyond the Proof of Concept (POC) stage.
One of the main issues holding companies back? Large language models (LLMs) trained on public data lack the specialized business knowledge necessary to support company-specific use cases, creating a gap.
Some companies attempt to tackle this challenge by fine-tuning an LLM with internal data. But fine-tuning can be hard to get right, costs money, and takes time. As a result, companies often end up using generic models without context on their business.
Retrieval-augmented generation (RAG) has emerged as a viable approach, combining LLMs with targeted data retrieval for more accurate and relevant AI outputs. It lets users chat with internal data and unlock various enterprise use cases. This includes answering customer service questions in real-time, improving sales enablement, analyzing data for better decision-making, and simplifying compliance processes.
But many teams remain stuck in “POC purgatory” as they struggle with generating consistently accurate answers.
This is where graph-based RAG comes in. Unlike traditional approaches to RAG that use vector databases, which can suffer from high hallucination rates and crude data handling, graph-based RAG delivers the accuracy and depth needed for practical applications. It’s not just better — we believe it’s the only scalable solution for generative AI in business contexts.
How graph RAG stacks up to vector RAG for accuracy, scalability, and affordability
Both vector RAG and graph RAG are techniques used for achieving retrieval augmented generation. The main difference is how they handle data, especially when it’s complex and connected to other data in an enterprise.
Vector RAG: simplified but limited
Vector RAG approaches data retrieval by converting data points into numerical vectors, which are then stored in a vector database. This method is straightforward and powerful for simple semantic searches, like reviewing a corpus of similarly-structured documents.
But once you start adding structured and hierarchical data to the mix, vector RAG struggles to capture the intricate relationships and nuances. This is because vector representations can’t show complex data structures. This means they lose context and relationship information that’s important for understanding and retrieving hierarchical or connected data correctly.
In large business settings, data often exhibits complexity, with numerous interdependencies and layers of information.
For instance, electronic health records (EHRs) capture and organize detailed patient histories, including diagnoses, treatments, medications, and interactions with healthcare providers.
Vector databases, by focusing only on the numerical representation of data, may oversimplify these relationships. This can lead to retrieval results that are close in terms of numerical distance but lack the contextual understanding needed for accurate and relevant responses.
Graph RAG: captures complexity and context
In comparison, graph-based retrieval offers a more sophisticated approach to RAG. Instead of simply looking at the distance between data points, it builds a web of relationships between data points. All data points become nodes, and their relationships to other points become edges.
Enterprise data isn’t just about individual points — it’s about how those data points interact and relate to each other.
With graph-based retrieval, you’re not just finding the closest match — you’re finding the right match by understanding the deeper connections within your data.
Vector RAG: costly to scale
There are also the issues of rigidity and cost with vector-based RAG. Every time you need to add new data, a vector database can’t just append it to the existing dataset. It needs to rerun all the data and assign each data object a new value. This is because the entire dataset determines the value given to each vector embedding. And every time you change your embedding model, it costs money. The larger the corpus of data your company has, the more it’ll cost.
With new data added every day, an enterprise environment demands a more dynamic, flexible, and affordable solution.
Graph RAG: saves on costs as you scale
Unlike vector databases that require frequent, costly updates, graph RAG systems retain semantic relationships, which not only improves accuracy but also simplifies complex decision-making processes. This makes enterprises more agile and responsive without the hefty price tag.
Graph-based systems are designed to scale more smoothly, which is crucial for handling enterprise-level data. This scalability means that as the data grows, the system can handle it without a significant increase in cost.
The Writer graph-based RAG approach, Knowledge Graph, further boosts these cost advantages by being fast, cost-effective to implement, and allowing for easy, inexpensive updates.
Writer Knowledge Graph empowers teams to quickly and easily deploy graph-based RAG at scale
At Writer, our unique graph-based RAG solution, Knowledge Graph, has helped hundreds of enterprises move from POC to powerful, scalable implementations. Knowledge Graph grounds generative AI in your company-level context by connecting our platform to your internal data sources.
Get unmatched accuracy
Writer Knowledge Graph is a one-of-a-kind RAG solution and outperforms other approaches. In a benchmarking test, Writer Knowledge Graph took first in accuracy when compared with seven other popular RAG implementations using vector retrieval. It had an accuracy score of over 86 on RobustQA.
Derisk your AI strategy
Instead of slogging through months-long build cycles, fine-tuning LLMs, and stitching together multiple tools to build your own RAG, Knowledge Graph takes minimal effort to set up. Plus, it’s a key component of the Writer full-stack generative AI platform that helps you to build and deploy tailored AI apps in just days.
Stay secure and compliant
Knowledge Graph enables you to use your most confidential and important data with generative AI. Writer adheres to global privacy laws and security standards, such as GDPR, HIPAA, SOC 2 Type II, and PCI. We offer flexible deployment options for our platform.
Lower your costs
We’ve seen that Knowledge Graph can offer 67% lower costs compared to a DIY vector retrieval solution. Plus, as you scale, these savings become even more pronounced thanks to Knowledge Graph’s efficient handling and updating of data.
Bring speed and intelligence to every business workflow with Writer Knowledge Graph
Writer Knowledge Graph isn’t just a technological innovation — it’s a versatile tool that enhances efficiency and accuracy across multiple enterprise applications. From boosting customer support to empowering sales teams, its capabilities are making a significant impact.
Gather information from multiple sources
Handling multi-hop questions that require gathering information from various sources and conducting multi-step reasoning is another standout feature. This makes the Knowledge Graph a powerful tool for research and development teams, as well as for sales and sales enablement tasks. Imagine helping your company to reduce time spent on RFP responses by as much as 70%.
Get explainable, transparent answers to every query
Explainable AI is essential for building trust. Knowledge Graph chat sessions display the thought process behind each response, breaking down complex questions into manageable subquestions and highlighting specific source sections.
Deploy advanced knowledge assistants for any team
The Writer Knowledge Graph excels in advanced question-answering tasks. It acts as a knowledge assistant, interpreting complex queries, retrieving relevant information, and using logical reasoning along with summarization skills to construct comprehensive responses. This capability is invaluable for customer support and internal information retrieval systems. Some Writer customers have seen as much as a 30% decrease in time spent looking up answers to customer questions.
​Compare thousands of complex documents in seconds
Writer Knowledge Graph can analyze and identify differences and similarities between multiple documents. This is particularly useful for legal, academic, and corporate environments where precision is crucial. A tech firm is using Writer Knowledge Graph to power its e-discovery tool for lawyers, which minimizes the “human toil” of understanding vast corpuses of legal documents.
Transform knowledge into contextually relevant content
Whether it’s creating concise executive summaries or conducting due diligence on target list products, Knowledge Graph makes sure that content is accurate and contextually relevant, improving productivity across various departments. The proof is in popularity with end-users: One F500 financial services customer reports up to 70% improved advisor enablement when using a Writer-powered knowledge assistant.
Get started with Writer Knowledge Graph in five simple steps
Step 1: Identify the core use cases you want to start with
Knowledge Graph supports analysis, search, and question-answering at scale. Start by discovering what kinds of questions members of each team need to answer.
Step 2: Map the files and data sources you need to include
Discover the data sources teams are using today to answer the kinds of questions you expect them to ask your Knowledge Graph.
Step 3: Employ data hygiene
Is there any data that should be excluded? Garbage in, garbage out applies — make sure you’re only importing data that’s relevant and trustworthy.
Step 4: Set up your Knowledge Graph data connectors
Connectors make it easy to keep your company’s Knowledge Graph up-to-date and relevant. By syncing Knowledge Graph with your internal databases, you can guarantee your team is always working from the right source documents.
Writer employs OAuth apps to manage access to your data sources. Currently, we support data connectors for Confluence, Google Drive, SharePoint, and Notion, with additional options in development. You can configure these connectors through Writer-managed apps or your company’s self-managed apps.
Our API facilitates effortless management of Knowledge Graphs and their associated files. Whether you’re developing chat applications, recommendation systems, or other AI-powered tools, the KG API enhances and scales your development process efficiently.
Step 5: Build and deploy AI workflows connected to your data wherever teams work
Writer AI Studio simplifies the complexity of our full-stack generative AI platform. It provides an intuitive development environment that allows you to focus on building AI applications rather than managing a complex stack. Our flexible development options, pre-built templates, and comprehensive tutorials enable you to start quickly and scale efficiently.
Writer integrations for Slack, Chrome, Outlook, Mac, and more connect users of all technical levels with the tools they need. These integrations, featuring built-in graph-based RAG, help teams improve business workflows, make informed decisions, and enhance creativity and productivity.
Fast-track your enterprise AI strategy for success with Writer Knowledge Graph
Get started with Writer Knowledge Graph and lead your company into the AI era. Join industry leaders like Vanguard, Accenture, Qualcomm, and Vizient, who have transformed their enterprise data strategies with our advanced, secure, and explainable RAG technology. Request a demo today to see the impact firsthand.