Flagship case · getbo.shop
Bò — found by the machines that recommend it.
A premium Lisbon bone-broth brand built as an answerable commercial surface: clear category facts, location, product proof, purchase path, SEO/GEO structure, and a page buyers could trust after the recommendation.
What Bò is
Bò is a premium bone-broth brand in Lisbon: small-batch, direct-to-consumer, sold in glass jars, delivered locally, and priced in a category that needs explanation before the value is obvious.
Where the opportunity was
The opportunity was not only branded search. Buyers ask Google, ChatGPT, Claude, and Perplexity for products like 'best bone broth in Lisbon' before they know the brand. Bò needed to be legible at the category, location, product, and purchase-path level.
What changed
The site stopped acting like a decorative brochure and started acting like an answerable commercial surface: what the product is, why it is premium, where it is available, and how to buy it were all visible in the page itself.
How the page was built
The build used static Next.js output, semantic HTML, extractable claims, structured data, an llms.txt file, and a direct conversion path. The important facts did not depend on client-side rendering or hidden visual copy.
Why it worked
The same facts served both audiences: machines could classify and quote the offer, and buyers could verify the recommendation after the click. Category, location, process, container, delivery, and buying path were explicit.
What happened
Bò began receiving customers from Google, ChatGPT, and Claude without ad spend. Its own attribution settled around 15% of new clients from AI chats and around 5% from organic search.
What this does not prove
This is Bò's attribution, not a studio average or a ranking guarantee. It proves the mechanism: clear facts, crawlable structure, and a premium page can make a small brand easier to name, quote, and trust.