Apex GCC
Moved from occasional trailing mentions to consistent top-3 inclusion across enterprise prompts.
Illustrative example based on a composite of client engagements. Figures are representative, not a named client result.
The challenge in detail
Apex operated capability centers in three countries under a parent brand, two legal entities, and four campus-level identities. Group functions evaluating internal capability, and external buyers evaluating delivery partners, both started with AI assistants — and both received answers that described the country's outsourcing market generally rather than naming Apex. AI-driven inbound was effectively zero.
Diagnosis
The entity graph was fragmented beyond a model's ability to reconcile. Each region published its own capability narrative, headcount figures differed across sources, and the parent brand's description contradicted the centers' descriptions. Models responded exactly as expected under contradiction: they hedged and fell back on generic market statements.
What we did
We modelled a single entity hierarchy — parent, legal entities, campuses, capabilities, and people — and implemented it through validated schema and reconciled public records. Capability evidence was attached to the correct node rather than duplicated. A region-aware content layer replaced the translated global narrative, using the buyer language and citation sources specific to each market, and reporting was split per market from the outset.
Results and timeline
Average answer position across the enterprise prompt panel moved from 0.9 to 2.4 on a three-point inclusion scale, meaning the brand shifted from occasional trailing mentions to consistent top-three inclusion. Per-market reporting showed the gains were uneven — the strongest movement came in the market where entity contradiction had been worst, which is the typical pattern.
“Dhruvion treated AI visibility as the enterprise discipline it actually is.”
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