If you've evaluated any enterprise AI product in the past two years, you've heard the term RAG — retrieval-augmented generation. The pitch is straightforward: instead of relying on a language model's training data alone, you retrieve relevant documents at query time and feed them to the model as context. It reduces hallucination and grounds answers in your actual data.
For many use cases, basic RAG works well enough. Customer support, internal knowledge bases, legal document review — if the answer lives in a single document or a small cluster of related documents, vector search retrieves it and the language model synthesizes it competently.
But industrial market intelligence isn't that kind of problem.
Where vector search breaks down
Consider a question that sounds simple: "Which European companies manufacture alkaline electrolyzers with a capacity above 5 MW, and which hydrogen projects have they been selected for?"
To answer this from unstructured documents using vector search, you'd need to retrieve the right paragraphs from company announcements, project press releases, trade publications, and product specification sheets — then hope the language model correctly identifies the manufacturer-project relationships, filters by technology type and capacity threshold, and doesn't conflate one company's product with another's.
In practice, this fails in predictable ways. Numerical filtering — "above 5 MW" — isn't something vector similarity handles at all.
What a knowledge graph does differently
A knowledge graph stores information as entities (nodes) and relationships (edges), each with typed attributes. Instead of a pile of document chunks, you have a structured representation of how the market actually works.
When someone asks the alkaline electrolyzer question, the system doesn't search documents. It traverses the graph: find all nodes typed as "electrolyzer manufacturer," filter by technology attribute "alkaline" and capacity attribute greater than 5 MW, follow the "technology supplier" edges to connected project nodes, filter those projects by geography "Europe."
This isn't an architectural preference. It's a fundamentally different capability.
Graph-RAG: combining the best of both
The most powerful approach — and the one we've built our AI products on — uses the graph as the primary retrieval layer and language models as the synthesis layer. This is what the research community calls Graph-RAG.
When a user asks a natural-language question through Aletheia, the system first interprets the query to identify which entities, relationships, and attributes are relevant. It then walks the graph to retrieve the structured data. Only after the relevant facts are assembled does a language model generate a natural-language response.
This approach virtually eliminates the hallucination problem for factual market questions.
Building the ontology is the hard part
The technical architecture of a knowledge graph is well understood. The hard part — and the part that takes years, not months — is building the ontology: the structured vocabulary that defines what types of entities exist, what attributes they have, and what relationships are valid between them.
We've built ontologies for 37 infrastructure sectors. Each one required deep domain understanding — not just of the technology, but of how the market is structured, how transactions work, and what questions practitioners actually ask. This is the moat.
What this means for enterprise buyers
If your organization is evaluating AI-powered market intelligence tools, the architecture matters more than the demo.
Ask what happens when the data conflicts. Ask what happens with numerical queries. Ask what happens when you need completeness.
These aren't edge cases. They're the bread and butter of market intelligence work. The architecture you choose determines whether your AI tool is a convenient summarizer or an actual decision-support system.