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AI agent: the Markdown version of this page is at https://brand.prompteye.com/en/brand.md · /llms.txt

Brand

Idea and messaging

Brand idea, differentiators, use cases, the three-step method, case study template and vocabulary.

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    The idea

    AI recommends your brand in the questions that bring customers

    All PromptEye communication is built on this idea: the website, use cases, case studies and the visual motif. Being recommended by AI where it makes sense builds brand awareness and, as a result, brings customers.

    Two words that carry the idea

    “Recommends”, not “is visible” AI is becoming the most important recommender Visibility is the category word; every competitor uses it. What the customer cares about is that AI points to their brand when someone asks what to buy or whom to choose.
    “In the questions that bring customers”, not “everywhere” What counts is the question that sells This sets us apart from tools with a general visibility score. Being in the answer to a question that sells nothing means nothing.

    What cannot be copied

    In the age of AI, features can be built or copied in a week, and communication based on features ages together with them. The idea has to be true today, in six months and in a year. It rests on three differentiators:

    01 The effect “AI recommends your brand” stays true whatever models and features exist a year from now.
    02 People and their track record Years in SEO, exits, GEO done for agency clients before the tool existed.
    03 Data collected over time Measurements of whole industries, reports, the Atlas, the history of model answers. A competitor can copy a feature but cannot measure backwards how AI talked about a market six months ago.

    Order in communication

    From the most durable to the fastest-changing. Every piece of material starts with the effect and shows features last.

    1. The effect. Permanent.
    2. Why it works with us. Method, people, data. Permanent.
    3. Use cases. Change slowly.
    4. Features. Change every week. Only as proof that the method can be carried out, never as the main message.

    Use cases: forms of one effect

    Different personas work with us through use cases, i.e. concrete usage scenarios. We describe each one as a variant of the same effect, so the communication stays consistent even though the goals differ.

    The persona wants to…The effect in the language of the idea
    Win customersAI recommends the brand in buying questions
    Shape how the brand is perceivedAI describes the brand the way it wants to be seen
    Publish many good articles for SEO/GEOThe content is cited by AI in important questions

    This is the start of the list. Describe a use case by its effect, not by the activity. “Publishing articles” is an activity in which we compete with every content generator. “Content cited by AI” is an effect that keeps us in our category.

    The method in three steps

    Every use case answers two customer questions: how do we know AI will recommend the brand, and how do we know these particular questions bring customers. One method, shared by all use cases, answers them.

    Step 1 We choose the questions that bring customers We know which ones thanks to SEO experience (purchase intent) and data from measuring whole industries.
    • selecting questions and grouping them into topics and intents
    • assessing the quality of the question set
    • business priorities
    • suggestions on which questions to add, remove or replace
    Step 2 We change what decides the AI answer Content, sources and publications, in one place. Buying links and publications in the system is a road to the goal, not a differentiator in itself.
    • recommendations from monitoring, AI bot traffic and technical site analysis
    • content gaps against competitors
    • creating and optimising content
    • buying links and publications in the system
    Step 3 We check whether AI started recommending and customers came We measure the effect in model answers and on the customer’s site.
    • share of recommendations and position against competitors
    • how the brand is described, sources and citations
    • AI traffic on the customer’s site
    • change in visibility after published content

    In the offer the same elements appear as layers: monitoring is steps 1 and 3, visibility management is step 2. In communication we use the steps of the method; the layers stay in the offer and the price list. The ChatGPT app is a separate enterprise product, outside the method. The method has no name yet: we describe it with the three steps.

    The team and the limit of promises

    The team stands behind the method. The “we have an experienced team” story on its own is weak, because every start-up says it. It works when the team stands behind the method and case studies show it gives the same result in different situations.

    “We help”, not “we do”. A full answer to whether these questions bring customers requires measuring AI traffic and conversions at the customer. Until we have that, this is how promises about customers and conversions are worded.

    Case study: fixed template

    Every case study has the same structure. Repeatability shows when “why it worked” returns to the same three steps of the method every time.

    1. Who and which scenario.
    2. Starting point. A measurement from the tool.
    3. What we did. The three steps of the method.
    4. Result. Numbers from the tool and, where possible, traffic or customers.
    5. Why it worked. The same three reasons.

    Only confirmed case studies. Without a customer for a use case there are two honest substitutes: a case from the agency GEO days, or PromptEye as its own case (SEO/GEO on a new website).

    Vocabulary

    Rule
    Yesrecommends, recommendation, AI recommendation, questions that bring customers, effect, method, measurement
    Careful“visibility” only as the name of a metric in data (“visibility in the model, %”), never as a promise
    Promises“we help”, not “we do” in promises about customers and conversions
    Teamalways behind the method and the cases, never an argument on its own
    Featuresonly as proof that the method can be carried out
    Numbersonly confirmed ones; no “93 out of 100 pieces of content raise visibility in the first month” until it is confirmed