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We test how ChatGPT, Gemini, Perplexity, and Claude respond today when travelers ask about your destination, your category, or your direct competitors.
You receive a full report showing: where you are currently visible and where you are 'invisible' to AI; the specific prompts that are currently losing you bookings or visitors to the competition; and a roadmap to claim your AI Share of Voice. No commitment required.
Shopping Research is a feature in ChatGPT that acts as a personalized shopping assistant.
Simply describe what you’re looking for, such as “a lightweight laptop for travel”, and ChatGPT gathers product details, reviews, specs, prices, and comparisons from the web.
You can refine the results by marking products as “Not interested” or “More like this”, helping ChatGPT understand your preferences.
At the end, you receive a custom buyer’s guide that explains the pros, cons, and trade-offs of each option, making your purchase process easier and more informed.
Training a Large Language Model involves feeding it enormous volumes of text data, from books and blogs to academic papers and web content.
This data is tokenized (split into smaller parts like words or subwords), and then processed through multiple layers of a deep learning model.
Over time, the model learns statistical relationships between words and phrases. For example, it learns that “coffee” often appears near “morning” or “caffeine.” These associations help the model generate text that feels intuitive and human.
Once the base training is done, models are often fine-tuned using additional data and human feedback to improve accuracy, tone, and usefulness. The result: a powerful tool that understands language well enough to assist with everything from SEO optimization to natural conversation.
Entity-based SEO helps AI systems understand who a company is, what it offers, and how it relates to other concepts in an industry. For B2B organizations, strengthening entity signals and semantic relationships increases the likelihood of being recognized as an authoritative source in AI-generated search results.
Content that is well-structured, informative, and organized around clear topics is easier for retrieval systems to access and use. Structured headings, semantic clarity, and authoritative information increase the chances that content will be retrieved and used by AI systems during response generation.
The "Shop Similar" feature is one of the most commercially significant additions to Google's Search Generative Experience. It bridges the gap between inspiration and purchase in a single, seamless flow.
Here's how it works:
The user never needs to reformulate their query, run a reverse image search, or navigate to a separate shopping tab. The entire journey, from idea to purchasable product, happens within the search interface.
Key distinction: The matching logic is visual and semantic, not purely keyword-driven. This means that the quality and accuracy of product imagery now plays a direct role in whether a product appears in these AI-matched results.
What this means for retailers: Products that are well-represented in Google's Shopping Graph, with accurate metadata, competitive pricing, and high-resolution imagery, are far more likely to be surfaced. Brands that invest in structured product data and visual quality will have a measurable advantage in this new shopping experience.
We run your target traveler prompts across every major AI platform on a weekly basis, tracking exactly where and how your brand or destination is mentioned.
You receive a live dashboard showing: your AI Share of Voice compared to direct competitors; citation trends and brand sentiment; and which specific prompts are driving high-intent traffic to your official channels.
