The impact of location on AI responses
AI models generate different responses based on the geographic location and IP address of the user. Tracking your visibility from a single location provides an incomplete view of your global brand health because LLMs prioritize regional relevance, local competitors, and territory-specific sources.KIME allows you to simulate queries from specific countries to see exactly what local customers experience when interacting with AI.Localized tracking identifies if your brand is being recommended correctly in international markets or if regional competitors are displacing your visibility. This is critical for global brands where market share and sentiment vary significantly by territory. By tracking these variations, you can adjust your content strategy to improve performance in specific regions.
Regional API routing
KIME uses a specific technical mechanism to ensure regional accuracy across all supported models. When you select a location, the platform requests the API for the LLMs directly from that selected region. This process ensures the generated output is the closest possible match to what a real user in that specific country would see on their own device.Current Availability: KIME currently supports localization at the country level. This allows you to track market-specific performance across different regulatory and cultural landscapes.
How to localize your prompts
You can set a location for any prompt during the creation process or by editing an existing query in your library. The platform provides a selection menu with flags to help you quickly identify and select the correct territory.Select the target country
Choose the country where your target audience is located. KIME will automatically route the query through the corresponding regional node.
Translation and terminology
To achieve accurate results in non-English speaking markets, you must write prompts using the terminology and phrasing used by local consumers. Simply translating a keyword is often insufficient because AI models respond to the natural language nuances of the specific region. You should focus on how users in that market actually communicate with LLMs.- High-quality translation: We recommend using DeepL for high-quality translations to capture the natural language nuances required for accurate AI responses.
- Local intent: Ensure your prompts reflect how a local user would actually speak to an AI. For example, use “trainers” in the UK versus “sneakers” in the US to trigger the most relevant AI responses.
- Testing and validation: Use the Latest Model Answers widget on your dashboard to verify that the localized AI response aligns with the language and context of your chosen territory.