In the understanding of entrepreneur Hugo Galvao de Franca Filho, many pet retail listings are still written for a type of search that is starting to lose ground: titles packed with stacked keywords and short descriptions, designed to show up when customers type in loose terms. The alternative gaining ground is conversational search powered by artificial intelligence, in which the buyer asks a complete question and a virtual assistant answers, compares products, and suggests options.
This movement already has concrete examples. Amazon launched in the United States a shopping assistant built directly into the search bar, capable of answering complex questions, comparing products, and personalizing suggestions, replacing its previous tool, called Rufus. The launch was initially limited to American users, but it points to where the shopping experience on major platforms is heading. For those who sell pet products, this is a good time to review listings with a different reader in mind.
From keywords to complete questions
In traditional search, a pet owner types something like “large dog feeder” and scrolls through dozens of results. In conversational search, they describe the real situation: they need a feeder for a 30-kilogram dog that eats too fast and knocks the bowl onto the floor. Hugo Galvao interprets this change as a reversal of responsibility, because it is no longer up to the buyer to filter the listings but up to the assistant, which only recommends what it can understand.
This difference changes what makes a listing competitive. A title loaded with repeated terms may help in keyword search, but it does not answer whether the product is suitable for a dog of that size, whether it is non-slip, or whether it is dishwasher-safe. If the information is not written down, the assistant has no way of stating that the product meets the request, and it is likely to recommend a competitor that made this information clear.
What a shopping assistant needs to find
To be recommended, a listing needs to answer the questions an owner would ask an experienced salesperson. In the pet segment, this includes the recommended size and weight, the animal’s age range, exact dimensions, material, cleaning instructions, and use cases. In the analysis of Hugo Galvao de Franca Filho, the description stops being a sales pitch and starts working as an answer sheet, organized so that both people and systems can find each piece of information effortlessly.
Customer reviews also factor into this reading. AI shopping assistants summarize what buyers say about a product and use those opinions to compare it with alternatives. A feeder with many reviews mentioning large dogs is likely to have a better chance of appearing in response to a question about large dogs. For this reason, encouraging detailed reviews after delivery becomes valuable not only as social proof, but also as information that feeds recommendations.
Artificial intelligence has also reached sellers
On the other side of the screen, platforms have started offering similar tools to sellers. Mercado Livre, for example, provides artificial intelligence features that generate titles and descriptions, edit photos, and automate answers to buyers’ questions. Hugo Galvao acknowledges the time these tools save, especially for stores with large catalogs, where reviewing listings one by one would take weeks and questions pile up throughout the day.
The key is in the review. Automatically generated text can sound convincing and still contain a wrong measurement or a use recommendation the manufacturer does not make, which in the pet market affects the animal’s well-being. The tool speeds up the draft, but responsibility for the information remains with the store. A simple routine eliminates much of the risk: checking technical data against the packaging or with the manufacturer before publishing.
Trust still decides the recommendation
With assistants increasingly able to search, compare, and even complete purchases autonomously, competition among sellers is likely to become more objective. Reputation, delivery times, review history, and clarity of information come to weigh as criteria that machines can measure. This applies to marketplaces and also to the company’s own online store, such as www.enjoypets.com.br, whose product pages can be read by external search engines and assistants in the same way.
In this scenario, the good news is that the practices that appeal to assistants are the same ones that have always appealed to pet owners: complete information, on-time delivery, and attentive customer service. Hugo Galvao de Franca Filho sums up the change plainly: a store that writes to help customers decide is likely to be better prepared to be recommended, while one that writes only to show up in search results runs the risk of becoming invisible precisely when the buyer asks.
