webAI’s Knowledge Graph RAG Sets a New Standard in Enterprise AI

‍Many companies offer Knowledge Graph RAG, but none match the accuracy, depth, and performance of the webAI solution. 

This is the first in a series of posts about our new Knowledge Graph RAG solution. Here, we introduce the core concepts and the industry-leading functionality of our solution. 

Future posts will dive deeper into the RobustQA benchmarking results that set us apart, explore specific industry use cases, and unpack the details of the technology itself.

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Generative AI promises to transform industries, but every enterprise quickly learns the same painful lesson: Even the most advanced LLMs stumble when asked to process proprietary documents filled with images, tables, and complex diagrams. They miss crucial context. They doom scroll. They hallucinate. They fail exactly when you need them most.

Knowledge Graph RAG changes this fundamentally.

What Is RAG?

RAG stands for retrieval-augmented generation. It’s a simple but powerful concept:

1. Retrieve: The AI finds relevant context from your own trusted documents.
2. Augment: That context is fed into your LLM, enriching its understanding.
3. Generate: The LLM produces answers grounded directly in your source material.
   
   

Here’s the catch: traditional RAG systems can only handle clean, text-based documents. 

Real-world enterprise documents are never that neat.

The "Messy Document" Challenge

Enterprise documents—manuals, technical specifications, contracts, safety procedures—aren't just paragraphs of text. They're messy. They’re dense. They contain tiny diagrams, detailed tables, scanned images, even handwritten annotations.

Most RAG solutions stumble badly here. Traditional Optical Character Recognition (OCR) add-ons can’t reliably capture this complexity. Text chunking and embedding approaches lose crucial context.

Simply put: Messy documents break traditional RAG pipelines.

Why Our Knowledge Graph RAG Leads the Industry

Knowledge Graph RAG combines retrieval-augmented generation with a structured knowledge graph, enabling AI models to find relevant context from your documents and deliver precise, accurate answers. 

While this sort of multimodal ingestion has become standard, what sets our solution apart is how we uniquely blend vision and language models directly within a proprietary knowledge graph. Rather than treating documents as linear text or separate image snippets, our graph-driven approach creates rich, high-dimensional relationships between textual and visual entities. This fusion allows our system to interpret subtle visual cues alongside textual context, uncovering nuanced connections competitors routinely miss.

Additionally, our solution isn't an assembly of open-source components; it's proprietary IP built from the ground up after standard off-the-shelf approaches fell short. Engineered specifically for enterprise scalability, it runs securely and efficiently either on-premises or via private cloud, offering unmatched accuracy without compromising control.

Benchmarks Are Good, But Not the Whole Story

We recently achieved the highest recorded accuracy on the text-only RobustQA benchmark: 94.1%. This result exceeds the previous state-of-the-art by over 7 points, and we know our system is capable of even better performance.

Benchmarks like these offer useful validation, but they focus solely on clean text data. We don't over index on these results precisely because real-world enterprise use cases are far messier and more complex.

We'll unpack our industry-leading RobustQA results in greater detail in the second post in our Knowledge Graph RAG series.

What Really Matters: Real-World Performance

We design rigorous tests specifically to challenge retrieval solutions, including questions involving diagrams, tables, and intentionally tricky scenarios—such as asking for details that don't exist in the document. These tests feature a comprehensive set of visual data points and precise, nuanced queries.

For example, we tested our Knowledge Graph RAG performance against ChatGPT o3 using an exceptionally complex 900-page F-18 flight manual dense with images and diagrams.

When asked, "Diagram 5-4 lists a base altitude for the approach procedure. What is it?" (when no base altitude was listed), ChatGPT o3 struggled, ultimately guessing inaccurately after extensive searching. Our Knowledge Graph RAG, however, confidently responded, "There is no base altitude listed in the diagram."

In another instance, we queried a specific numeric detail embedded in a complex table. ChatGPT o3 was unable to parse the data correctly and provided an incorrect answer. In contrast, our Knowledge Graph RAG accurately extracted the precise numerical value from the embedded table, demonstrating superior multimodal comprehension.

Those are just two examples. The overall results are telling.

These aren't carefully curated benchmarks. They’re tough, messy, real-world tests designed to challenge the limits of AI retrieval systems. In high-stakes, life or death situations, accuracy isn't just nice to have; it's absolutely essential.

Detailed explorations and visual examples of the F18 test and others will be shared in upcoming posts.

Enterprise-Grade Advantages

Knowledge Graph RAG doesn’t just deliver accuracy. It’s built for enterprise readiness:

• Runs securely on-premises or via private cloud.
• Scales effortlessly across thousands of complex documents.
• Answers reliably with sub-second retrieval speeds.
  
  

The net out: You get better answers faster and maintain total ownership and control.

Want to See Knowledge Graph RAG in Action?

Ready to see how our KG RAG solution can transform your organization's document processing capabilities? ‍

Sign up for our upcoming webinar!

How Leading Manufacturers Are Using Private AI and Knowledge Graph RAG to Power SOPs, QA, and Inspections

August, 27th, 2025 at 2pm EST

The webinar will feature live demonstrations, explore real-world enterprise applications, and show off detailed head-to-head comparisons with leading AI solutions. Experience firsthand how our KG RAG approach transforms complex document processing across industries.