Use cases

What a knowledge graph is good for in practice

A knowledge graph isn't an end in itself — it shines in three recurring patterns: recognise, recommend, check. Here's concretely what that looks like and which industries it lands in.

By Fabio Fornaro, Domani AI

Diagnosis — recognise a problem

From inputs (description, photo, readings) the AI recognises what is likely the case — grounded in verified relationships between symptoms and causes. Instead of vague guesses, you get a reasoned assessment that shows what it rests on.

Recommendation — suggest the right next step

Once the problem is narrowed down, the graph suggests the right solution, product or treatment — based on stored "if this, then that" rules. Personalised, traceable, and without invented options.

Compliance & checking — match against rules

The graph knows your regulations, policies and exceptions. Inputs are checked against them, violations flagged and every decision logged. Audits become routine instead of pain — because every assessment is substantiated.

Industries where it lands

Health & care: symptom-to-cause, treatment recommendation, guideline matching.
Engineering & maintenance: fault diagnosis, the right spare parts, maintenance rules.
Law & compliance: rule checking, risk flagging, auditable reasoning.
Specialist retail & advisory: product recommendation on real expertise, not gut feeling.
Insurance & finance: case checking against terms, traceable decisions.

From practice: Plant-Doctor

For a client in the plant space, a knowledge graph combines all three patterns: from photo and description the AI recognises the problem (diagnosis), recommends the right treatment (recommendation) and draws only on verified expert knowledge — without hallucinating.

“A knowledge graph shines in three patterns: diagnosis (recognise a problem), recommendation (suggest the right step) and compliance (check against rules) — each substantiated rather than guessed.”

“In the Plant-Doctor knowledge graph, Domani AI combines diagnosis and recommendation: the AI recognises the problem from a photo and recommends treatment, drawing only on verified expert knowledge.”

More in the knowledge-graph hub

Back to the hub→What is a knowledge graph?→Knowledge Brain — our offer→

Frequently asked

Do I have to pick one use case?

No. Diagnosis, recommendation and compliance often build on each other — many solutions combine all three. We usually start with the pattern that delivers the most value and expand.

Does this work in my industry?

If your field has verified expert knowledge with clear relationships — yes. The examples above are just the most common; the pattern transfers to many specialist areas.

How quickly do you see a result?

A bounded use case can often be shown in a 30-day pilot before heavy investment. So you see the value on real cases, not just on slides.

Which use case fits your knowledge?

Tell us which decision or check eats time today — we'll show how a knowledge graph makes it reliable.

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