When vector search is not enough: company knowledge as a knowledge graph

Almost every AI platform today offers "chat with your documents": upload files, ask questions, get answers with a source. Technically that is usually RAG (retrieval-augmented generation) with vector search — and for many questions it works excellently.

Just not for all of them. Understanding where the line runs leads to better decisions when introducing AI in a company.

What vector search is good at

Vector search finds passages that are semantically similar to a question. That makes it strong on factual questions whose answer exists somewhere as a paragraph:

• "What notice period does the contract with company X specify?"
• "What does our travel policy say about first-class rail travel?"
• "What is the warranty condition for product line Y?"

The answer sits in one or two document sections. The search finds it, the model summarises it, done.

Where it fails systematically

It gets difficult when the answer does not exist as a passage but only emerges from relationships across many documents:

• "Which of our customers are affected by the discontinuation of product Z?"
• "Who owns which process in onboarding — and where are the gaps?"
• "Which contracts run through suppliers who also supply competitors?"

For such questions vector search would have to link dozens of findings correctly — in practice that breaks down: it returns the most similar snippets of text, not the structure behind them.

The company graph

A knowledge graph models company knowledge as a network of entities and relationships: people, products, customers, contracts, processes — and how they connect. Questions about relationships become path and neighbourhood queries in the graph instead of similarity searches in text.

The second advantage is visibility: a graph can be visualised interactively. You see your own company's knowledge network — including the gaps and the legacy nobody had noticed before.

Not an either-or

Important: graph and vector search are not competitors but tools for different kinds of question. Factual question → document search. Structure and relationship question → graph. A good platform offers both and lets the AI pick the right tool per question.

That is exactly how Futureway AI is built: documents are indexed automatically (including scans via text recognition) and are searchable — and on top of that the platform builds a queryable, interactively visualised company graph from your knowledge. Both run, like everything with us, with 100 % of AI processing in the EU.

Curious what your company knowledge looks like as a graph? Get started with Futureway AI — pick a tier, cancel monthly.