Community/Introduce yourself

    Hi everyone 👋 I’m Gabriel, building Glaudimax AI

    hugo acevedo
    hugo acevedo1mo ago

    `Hi everyone! I’m Gabriel from Uruguay 👋

    I’m building Glaudimax AI, an AI Buyer Simulator for ecommerce.

    I started it because AI agents are beginning to discover and understand products differently than human shoppers. I wanted a simple way for store owners to see their product pages through an AI buyer’s eyes.

    You paste a product URL, Glaudimax analyzes the publicly detectable commerce signals and gives you an AI Buyer Score /100 with actionable recommendations.

    The MVP is live and I’m currently looking for real ecommerce stores to test it with and improve it from actual feedback.

    Happy to meet other founders here and exchange feedback! 🚀`

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    Comments (6)

    Sergey KargopolovSergey Kargopolov1mo ago

    Welcome to the community, Gabriel! 🙋🏻‍♂️

    I think the only e-commerce store that we have here is https://saashive.com/products/crazy-sajangnim by @Jinny Moon. Hopefully he will be interested.

    hugo acevedohugo acevedo1mo agoReply

    Thanks Sergey! Really appreciate the welcome and the introduction 🙌 I’ll check out Jinny’s store. This is exactly the kind of real ecommerce use case I’m looking for to test Glaudimax AI. Thanks for pointing me in the right direction!

    Sergey KargopolovSergey Kargopolov1mo agoReply

    ↳ Replying to hugo acevedo

    You are very welcome, Gabriel 🙏🏼

    Jinny MoonJinny Moon1mo ago

    Welcome, Gabriel! Glaudimax AI sounds interesting, especially the idea of evaluating a product page from the perspective of an AI buyer rather than only a human shopper.
    I think the most important part will be making the score feel trustworthy and actionable. A /100 score can be useful, but I’d want to understand exactly what signals lowered it and which changes are most likely to improve how AI systems interpret or recommend the product.
    I’m also curious how different the results are across product categories, since an AI buyer may need very different information for fashion, electronics, or software.
    Four things I’d be interested to learn as you develop it further: how you validate that the score reflects real AI-agent behavior, which signals have the biggest impact, whether you plan to compare products against competitors, and whether stores can re-scan pages to measure improvement over time.

    hugo acevedohugo acevedo1mo agoReply

    Thanks Jinny! This is exactly the kind of feedback I was hoping to get here. 🙌

    You’re right about the score — making it transparent and actionable is much more important than simply showing a number. The current MVP already shows which detectable commerce signals pass or fail and gives recommendations, but validating those signals against real AI-agent behavior is definitely an important next step.

    I also really like your point about different product categories and re-scanning after improvements. I hadn’t considered all four of those questions together, and they give me some very useful directions to test as Glaudimax evolves.

    Thanks for taking the time to look at the idea properly!

    hugo acevedohugo acevedo1mo ago

    Thanks Jinny! This is exactly the kind of feedback I was hoping to get here. 🙌

    You’re right about the score — making it transparent and actionable is much more important than simply showing a number. The current MVP already shows which detectable commerce signals pass or fail and gives recommendations, but validating those signals against real AI-agent behavior is definitely an important next step.

    I also really like your point about different product categories and re-scanning after improvements. I hadn’t considered all four of those questions together, and they give me some very useful directions to test as Glaudimax evolves.

    Thanks for taking the time to look at the idea properly!

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