How Inani Hotels Achieved 88.5% First-Position Rate in AI Search

Traditional search engines rank pages. AI engines synthesise answers. When a business traveller asks ChatGPT, Perplexity, or Google AI Overviews for the best hotel near a convention centre, the AI does not return a list of blue links. It constructs a narrative, citing specific properties by name, quoting precise facts, and recommending based on the information it can verify.

This shift fundamentally changes what it means to be visible online. Brands that provide vague, aspirational copy get ignored. Brands that provide precise, verifiable, and structured information get cited. Inani Hotels understood this distinction early and acted on it.

The result was a transformation from zero AI visibility to dominant first-position placement within weeks of the November 2025 pilot launch. This case study documents the methodology, the metrics, and the strategic principles that made it possible.

AI models are trained on billions of data points, but they prefer structured, unambiguous information when constructing answers. Vague marketing copy like 'conveniently located nearby' or 'a short drive away' gives the AI nothing to cite. Precise facts like '180 metres from the main gate' or 'a 2-minute walk' give it everything.

The core principle behind Inani's success is simple: replace every piece of vague copy with a citable fact. Every claim must be verifiable. Every description must be specific enough for an AI model to quote with confidence. The difference between being filtered out and being ranked first often comes down to specificity.

Across 25 tracked prompts spanning three AI providers (ChatGPT, Perplexity, and Google AI Overviews), Inani Hotels appeared in the first cited position 88.5% of the time. This is not a vanity metric. First-position placement in AI responses correlates directly with user trust and click-through behaviour. For Gallagher Convention Centre queries specifically, Inani achieved 70% AI visibility, while in the Pretoria CBD legal travel niche, the brand secured a 30% visibility share. Large national hotel groups, including City Lodge, Tsogo Sun, and Legacy Hotels, recorded zero visibility for those same queries.

AI search responses typically cite between two and five sources. The first-cited source receives disproportionate attention because AI models structure their answers in a narrative flow where the primary recommendation anchors the entire response. Being first is not just visibility; it is authority.

The 88.5% rate was achieved through consistent structured data, a content strategy built around citable facts, and systematic monitoring using NUDG3's AI visibility platform to identify and fill gaps in real-time.

During the campaign period, Inani Hotels recorded a 233.46% increase in direct website traffic. This was not broad organic growth. The traffic profile showed characteristics of AI-referred visitors: high intent, longer session duration, and significantly higher engagement with booking pages.

AI-referred traffic behaves differently from traditional search traffic. When a user receives a specific recommendation from ChatGPT or Perplexity, they arrive at the website with pre-formed intent. They have already been told why the property is relevant to their query. The AI has already done the persuasion.

This makes AI-referred traffic among the highest-converting in digital marketing. The 233.46% increase was not just more visitors. It was more visitors who were already primed to book. Notably, much of this traffic appears as 'dark traffic' because AI applications often strip referral data, meaning the true AI-driven impact is likely higher than what analytics platforms report.

Source Authority measures what percentage of the URLs cited by AI models for a given topic point back to the brand's own properties. For Inani Hotels, 39.2% of all AI-cited sources linked directly to Inani-owned domains rather than third-party platforms like Booking.com or TripAdvisor.

In the hospitality industry, this metric is transformative. Hotels have historically depended on Online Travel Agencies (OTAs) for visibility, paying 15-25% commission on each booking. When AI models cite the hotel's own website as the authoritative source, the booking path bypasses OTAs entirely.

Achieving 39.2% source authority means Inani Hotels controls nearly two-fifths of its own AI narrative. Every citation that points to an owned domain is a booking that does not carry an OTA commission. At scale, this represents a significant shift in revenue attribution.

The results above were not accidental. They were the product of a systematic Generative Engine Optimisation (GEO) methodology built on three pillars: natural language precision, structured data optimisation, and niche dominance strategy.

Every page on the Inani Hotels website was rewritten to replace subjective claims with objective, citable facts. Room descriptions included exact measurements, view angles, and amenity counts. Restaurant pages listed specific cuisine types, chef credentials, and guest satisfaction metrics.

This approach aligns with broader industry observations that content containing precise statistics significantly improves AI visibility. The AI model needs something concrete to quote. Precision gives it that anchor.

JSON-LD schema markup was implemented across all key pages, providing AI crawlers with machine-readable context about the property. Hotel schema, LocalBusiness schema, and FAQ schema ensured that every relevant data point was available in a format optimised for AI consumption.

Structured data acts as a direct communication channel with AI models. While the AI can infer information from unstructured text, schema markup removes ambiguity and ensures the correct facts are surfaced in AI-generated responses.

Rather than competing for broad terms like 'best hotel in South Africa', the strategy focused on owning specific niche queries. Prompts like 'best hotel near the Gallagher Convention Centre', 'accommodation for legal teams working in Pretoria CBD', and 'independent hotels suitable for company projects' were identified as high-intent, low-competition opportunities.

By dominating these specific queries, Inani Hotels built compounding authority. Each successful citation reinforced the brand's relevance for related queries, creating a flywheel effect where early wins generated progressively broader visibility.

The Inani Hotels case study demonstrates a fundamental truth about AI search: precision beats budget. You do not need the largest marketing spend to dominate AI visibility. You need the most precise, structured, and verifiable content in your category.

AI models are not influenced by ad spend or domain authority in the traditional SEO sense. They are influenced by the quality, specificity, and structure of the information they can access. An independent hospitality group with just two properties can outperform national chains with hundreds of locations if its content is more precise and better structured.

The brands that will win in AI search are the ones that treat their content as infrastructure, not decoration. Every fact is a citation opportunity. Every structured data point is a signal. Every precise description is a competitive advantage that compounds over time.

These statistics reflect a focused pilot conducted in November 2025, tracking 25 targeted prompts across three AI providers. This is not a broad market picture. It is a defined, controlled sample demonstrating what targeted GEO effort achieves. The two Inani properties, Inani Gallagher in Midrand and Inani Morning Star in Pretoria CBD, served as the test cases against established national competitors.

NUDG3 is a dynamic AI visibility platform capable of tracking any number of custom prompts across any AI provider through a single precise dashboard. Inani Hotels represents one focused example of what targeted effort achieves. The platform's real power lies in continuous, scalable monitoring across unlimited prompts and providers for complete brand visibility. Inani is the proof of concept. NUDG3 is the engine.