Category explainer

Why AI recommends brands.

AI doesn't rank websites. It builds confidence.

Confidence comes from signals: consistency across sources; citations on trusted domains; expert and community mentions; entity clarity; repeated, recent evidence.

Engines assemble these into a Recommendation Graph per question, and name the brands with the deepest Evidence Footprint.

One good website is one node. The recommendation is won across the graph.

LinkinGrow measures that graph, expands your footprint across it, and verifies the result monthly.

The signals that build confidence

Consistency across sources

The same facts about your brand — described the same way — everywhere the engines look.

Citations on trusted domains

Named in publications and databases the engines already index as authoritative.

Expert and community mentions

Real practitioners talking about you in the venues buyers gather in.

Entity clarity

Your brand, products, and people cleanly identifiable in structured knowledge graphs.

Repeated, recent evidence

Not just published once — sustained, dated, refreshed. Engines discount stale signals.

Cross-format footprint

Long-form, video, community threads, reviews — different engines weight different graphs.

Example

Two brands. One question. One gets named.

Brand A — website-only

Beautiful site. Great SEO. One node. Absent from the third-party sources the engine trusts. Not named.

Brand B — full footprint

Covered across publications, community discussion, review sites, and structured data. Named. Consistently.

Same category. Same product quality. Different Evidence Footprint. Different answer.

See your footprint. Free.

Three buyer questions in your category. One page showing where you stand.

Get the snapshot →