Allegro and Amazon in the AI Era - How to Increase Product Visibility on Marketplaces

On 7 May 2026 Allegro, Poland's largest marketplace, made its AI Assistant available to all users of its mobile app. During testing more than 600,000 people bought something with its help, and shoppers increasingly don't type a product name; they describe the purpose: who it's for, what it's for, what problem it should solve. Amazon is further down the same road - more than 300 million customers used its Rufus assistant in 2025, and the company estimates the additional sales attributable to Rufus at nearly $12 billion a year. Marketplace optimization is no longer just a fight for a place in search results - more and more often, a product has to convince an AI assistant that it fits a need the shopper described in their own words.

Allegro's AI Assistant has been open to everyone since 7 May 2026, more than 300 million customers used Amazon's Rufus in 2025, and Amazon blocks ChatGPT's bots. How Allegro relevance works, how to prepare listings for AI assistants, why to combine a marketplace with your own store and how to keep one source of product data.

There's another side to this shift. Since November 2025 Amazon has been blocking ChatGPT's search bots, and later other external shopping agents. A product that sells well on Amazon may therefore not exist in ChatGPT's answers. In this post I show how visibility works on Allegro and Amazon today, what AI changes on both platforms, why it's worth running a marketplace and your own store in parallel, and how to keep a single source of product data for every channel.

Three layers of product visibility

On a marketplace, whether a shopper sees your product is now decided by three different mechanisms. Each requires a slightly different approach to the listing.

1. 1.The platform's search engine. The classic results list after typing a phrase and filtering - "Relevance" sorting on Allegro, search results on Amazon. Here the title, attributes, price, delivery and sales history count.
2. 2.The AI assistant inside the platform. Allegro's Assistant and Rufus answer descriptive questions and suggest products. Here what counts is whether the listing data makes clear what the product is for and who it's for.
3. 3.External AI assistants. ChatGPT, Gemini or Perplexity, when someone asks them what to buy. Here what decides is whether the assistant can access the listing page at all - and in Amazon's case it often can't.

How Allegro search works

Allegro's default sorting is "Relevance" (Trafność). According to Allegro's official help pages, a listing's position depends mainly on buyer engagement, meaning how interest in the listing changes over time (how often it appears in results, how often it's viewed and bought), and on the predicted delivery time - next-business-day delivery is rewarded. That means Allegro rewards listings that already sell and reach the customer quickly; a well-written listing is a requirement, but not a guarantee of a high position.

In practice, a few things you can control affect relevance:

• A title of up to 75 characters - brand, model, key feature and the phrase buyers type. No words like "hit" or "deal" that say nothing about the product.
• A complete set of attributes - they work as filters. A listing without size, color or material disappears from results when the buyer narrows the list.
• Price including delivery and fast shipping.
• Sales quality - reviews, complaints, on-time shipping.

Allegro is tightening its grip on listing quality. According to industry write-ups, since February 2026 the platform has used AI to assess listings - checking the reliability of information, photo quality and readability, and catching empty marketing phrases in titles. On top of that comes the obligation to label AI-generated images under the AI Act - Allegro added a field to its API where the seller declares that an image was created with AI. How to generate such graphics lawfully I covered in the post on AI video and image generation.

What Allegro's AI Assistant changes

The assistant changes how people search. Instead of "hiking boots size 43", the buyer writes "I'm looking for boots for a multi-day autumn trek in the Tatras, I have wide feet". The assistant has to work out from the listing data whether the product fits that description.

A listing with only a title and photos gives the assistant nothing to assess - listings with complete attributes and a concrete description of use cases win. In practice:

• Fill in every category attribute, including the optional ones.
• Describe uses and limitations: what the product is for, who it's for, in what conditions, what it doesn't do.
• Write in specifics instead of adjectives: "18/10 stainless steel, 0.8 mm thick" instead of "top quality".
• Look after product reviews - one of the clearest quality signals for any recommendation system.

It's the same direction I see in AI search beyond marketplaces: content from which concrete facts are easy to extract wins. I covered it in more depth in the post on writing content for AI citations.

How Amazon works: search, COSMO and Rufus

On Amazon, sellers now deal with three connected systems: classic phrase matching in search, the COSMO knowledge graph, which links products to shopper intent based on what people searched for and bought together, and the Rufus assistant, which answers questions in conversation.

Rufus has been available in Germany, France, Italy and Spain, among others, since October 2024 - the markets Polish and other Central European sellers most often reach through Amazon. According to Amazon, customers who use Rufus are about 60% more likely to complete a purchase. Practical takeaways for a listing:

• Descriptive phrases instead of keyword lists. "Insulated stainless steel bike bottle" tells the system more than the string "bottle thermos steel bike".
• Bullet points with benefits and uses that answer shoppers' typical questions.
• Questions and answers and reviews - Rufus draws on them when answering questions about a product.
• Photos showing the product in use and enhanced brand content where available.

External AI assistants and marketplaces

This is where things have clearly changed. In November 2025, the same week OpenAI launched shopping research in ChatGPT, Amazon blocked the OAI-SearchBot and ChatGPT-User bots in its robots.txt file, and later extended the blocks to bots from other AI providers. It also sued Perplexity over the Comet browser, whose agent made purchases on users' accounts. In August 2026, however, an appeals court vacated the injunction, finding that it's the user, not the tool's provider, who accesses the site - and in September 2026 Amazon blocked Meta's shopping agent. As a result, Amazon listings largely don't make it into ChatGPT's recommendations, and external assistants send shoppers to other stores.

For a seller the takeaway is practical: don't assume that being on a marketplace means being in ChatGPT or Perplexity answers. Run a simple test - ask the assistants the questions your customers ask and check whether your products appear and where the links lead. It's worth checking Allegro for your category the same way, rather than relying on assumptions. How to run that measurement regularly I covered in the post on AI visibility monitoring tools.

Marketplace and your own store - a two-track strategy

These changes strengthen an argument that held before too: a marketplace and your own store play different roles and work best together.

A marketplace brings volume and shopper trust; your own store brings margin, data and independence from one platform's decisions. How to prepare your own store for Google and AI shopping I covered in the post on e-commerce SEO and GEO, and the protocols through which AI agents can buy directly from a store in the post on agentic commerce.

A single source of product data

A two-track strategy only works if product data is consistent across every channel. A different price, description or stock level on Allegro, Amazon and your own store tells AI systems the data can't be trusted - and gives customers a reason to complain.

The fix is one place where you maintain product data, with automatic sync to every channel. In Poland that hub is most often Base.com, which operated as BaseLinker until 2024. From one catalog you send titles, attributes, photos, prices and stock to marketplaces, your own store and Merchant Center. How to combine it with AI - for example to generate descriptions in each platform's format or to handle orders - I described in the posts on BaseLinker AI automation and order management automation. An automatically generated description should rest on catalog attributes, not the model's imagination - otherwise instead of better visibility you get complaints.

Reviews as fuel for recommendations

Every system described here - the platform's search, the internal assistant and the external one - uses reviews. That's where an assistant learns whether a product really works for the use the shopper is asking about.

Collecting reviews has a legal framework, though. In the EU, the Omnibus directive (applied in Poland since 1 January 2023) requires a seller who publishes reviews to state whether and how it checks that they come from people who bought the product. Publishing fake reviews or commissioning them is prohibited. The most effective and only safe method is systematically asking buyers for a review after delivery - with an automatic reminder and without rewarding only positive ratings. Reviews collected in your own store are also worth describing with structured data so they're readable for Google and AI assistants.

A step-by-step implementation plan

1. 1.Complete the attributes of all listings, starting with bestsellers and the highest-margin products.
2. 2.Rewrite titles on Allegro (up to 75 characters) and Amazon: brand, model, key feature, buyers' phrase, no empty slogans.
3. 3.Add uses and limitations to descriptions, in specifics rather than adjectives.
4. 4.Shorten shipping times where possible - on Allegro it's a direct relevance factor.
5. 5.Start collecting reviews after delivery, in line with the Omnibus directive rules.
6. 6.Label AI-generated images as the platforms require.
7. 7.Test the assistants - Allegro's Assistant, Rufus and ChatGPT - with your customers' questions and note which products and sources appear.
8. 8.Keep a single source of data and sync it automatically to every channel.
9. 9.Develop your own store as the channel for margin, data and visibility in external AI assistants.

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I help stores build visibility on marketplaces and in their own channel at the same time - from auditing listings and product data, through automating sync and descriptions, to measuring presence in AI assistants. I do this as part of e-commerce and AI for e-commerce services. Get in touch - I'll start with an audit of your 20 most important listings and a test of whether your products show up in AI assistants' answers.

Worth reading next:

• E-commerce SEO & GEO - How Stores Win Visibility in Google and AI Shopping
• Agentic Commerce - How to Sell When the Buyer Is an Agent (ChatGPT Checkout, ACP, AP2, UCP)
• BaseLinker AI Automation: How AI Integration Cuts Operational Costs in E-Commerce
• AI Order Management Automation - How AI Handles the Full Cycle from Cart to Complaint Without Manual Work