RLet's figure out how the creation of cards for marketplaces through a neural network works today, what tasks can already be automated, and why specialized services like Aidentika gradually changing the rules of the game.
Why a good product card sells better than advertising
MMany sellers continue to invest the bulk of their budget in promoting products, although the most expensive advertising cannot compensate for a weak card. Imagine the situation: a buyer enters the query “air humidifier” and receives several hundred almost identical offers. The price differs slightly, the characteristics are similar, and most sellers have high ratings. At this moment, the person has not yet studied the description, read reviews or compared the equipment. His brain makes the first decision solely based on the visual impression, which is formed in just a few seconds.
AndThis is why we can say that the product card today has become a new store window, only now it is not on the main street of the city, but in the search results of the marketplace.
EThis mechanism has long been studied by neuromarketing specialists. Studies using Eye Tracking technology show that people first pay attention to large visual elements, then evaluate the composition of the image, and only then begin to read the text. Scientists from the University of South Australia and researchers who published work in the journal PLOS ONE concluded that visual design directly influences the direction of the user's gaze and the sequence of purchase decisions.
PIn simple words, if the image could not stop a person’s gaze, then the likelihood that it will go to the card is sharply reduced. That is why today designers work not just with beautiful pictures, but with the psychology of attention, using color, composition, contrast and visual accents as full-fledged marketing tools.
XA good card should solve several problems at once. It must stand out among dozens of competitors, quickly explain the benefits of the product and help the buyer imagine how this product will be used in real life. Designers do thoughtful infographics, emotional use cases, images of the product in the interior or on a model, and sometimes even short videos. By the way, the ability to generate an AI picture or generate an AI video is already becoming a familiar part of the work of marketplace designers. All this helps to quickly convey the value of the product to the buyer and increase the likelihood of a purchase even before the person opens the product description.
PThis is the reason why the market for card designers continues to grow rapidly. A good specialist today understands not only the basics of composition and typography, but also knows the features of the Wildberries and Ozon algorithms, knows how to work with analytics, test different visual options and understand customer behavior. It is precisely such specialists that are in demand among large sellers, and many newcomers are wondering whether it is possible to make money on neural networks if the market is gradually automated. The paradox is that artificial intelligence has not reduced the demand for designers. On the contrary, it increased the demands on their competencies and allowed those who mastered the new tools to complete significantly more projects without increasing their working hours.
How to create a product card manually: 5 steps
EIf you look at the finished product card, it may seem that its creation takes very little time. The product is placed on the image, a few captions are added, and it looks like the job could be done in half an hour. Many sellers are surprised cost of designer services. However, behind each high-quality card there is a rather long production process, which involves not only the designer, but sometimes a photographer, a retoucher, a marketer, and even the seller himself. The more complex the product and the higher the competition in the niche, the more time you have to spend on preparing each image.
Bit all starts with preparation of the so-called product passport. At this stage, all the information that will be needed for the future card is collected: characteristics, advantages, features of materials, equipment, photographs, customer reviews and answers to the most common customer questions.
Zthen designer analyzes competitors. He studies the search results for Wildberries and Ozon, looks at what colors the category leaders use, what benefits are most often brought to the front screen, and what visual solutions have already become standard. Only after this does an understanding emerge of what the future design should be so that the card does not disappear among dozens of similar products.
Cthe next stage is already associated with visual part. If the seller does not have high-quality photographs, he has to organize subject photography or work with the material that is available. After this, retouching, background removal, color correction and preparation of sources begins.
Zthen designer determines the composition of future cards, selects a color scheme, creates infographics and thinks through the placement of text blocks. At this stage, the first layout appears, which is then refined several times together with the customer.
Pafter approval remains adapt images according to the requirements of marketplaces, check file sizes, image quality and compliance with the technical requirements of the site.
DEven for a relatively simple card, this entire process usually takes from one to three hours. If we are talking about a new product line with an individual photo shoot, complex infographics and several scenarios for using the product, the time easily increases to four to six hours for just one SKU!
A Now imagine a seller who displays a catalog of one hundred products on the marketplace. Even if several designers are working on a project at the same time, the total amount of work can amount to hundreds of hours. This is why large agencies often spend several months rather than weeks designing catalogues, especially when it comes to completely updating visual content.
TThis model was considered the only possible one for a long time. However, starting in 2024, the situation began to change rapidly. More and more designers have begun to use neural networks for marketplaces as assistants who take on the most labor-intensive and repetitive operations. At first, artificial intelligence helped write texts and generate ideas, then it learned to create images, and today professional, narrowly focused services have appeared that can automate almost the entire process of preparing cards.
They became the next stage of market development, because they allow you to reduce work time not by a few percent, but several times, while maintaining the quality of the finished result. For example, this card was created from one single photograph of a thermos in Aidentika neural networks. The designer additionally only prescribed the necessary characteristics for the infographic. Took less than two minutes.
Neural networks for graphic designers: from assistant to full-fledged co-author
EJust three years ago, most designers viewed artificial intelligence more as an interesting experiment. ChatGPT was used to generate ideas, Midjourney was used for beautiful pictures, and Stable Diffusion was used to experiment with backgrounds and styles. All this looked impressive, but was poorly suited for real work with marketplaces. The main problem was that universal neural networks did not understand the specifics of product cards. They could create a beautiful illustration, but did not take into account the requirements of Wildberries and Ozon, the features of infographics, composition restrictions and the tasks facing the seller. So designers still spent hours refining images by hand.
PThe turning point came in 2024–2026, when specialists stopped perceiving artificial intelligence as a separate service and began to integrate it into every stage of their work. Today, almost any marketplace designer uses several AI tools at once.
BLarge language models like ChatGPT or Cloude help you quickly create a card structure, come up with a catchy title, write an SEO description, select keywords and formulate the benefits of a product in human language. Image generators, Nano Banana for example, allow you to get dozens of composition options, try on a product in different interiors, quickly replace the background, or even generate a completely new scene. If earlier this required a full-fledged photo shoot, now a few minutes and the right task are enough.
How to do a photo shoot using a neural network and is it possible to make money using neural networks for photos?
OhHowever, real changes occurred after the emergence of specialized solutions developed specifically for sellers and marketplace designers. Services have begun to appear on the market that already understand the structure of the product card and know what result the seller needs. Among such solutions we can highlight Aidentika, Seller Moon, NeuroKholst, GenAPI, Neiro-card.ai and other platforms focused on automating individual card creation processes. Some help generate images in bulk, others work with texts, and others automatically adapt ready-made materials to the requirements of sites. Thanks to this, the designer no longer has to start from scratch every time. Artificial intelligence prepares the foundation, and humans do what really requires professional experience and taste.
For example, like this in Aidentika You can do a photo shoot without a person in a dress that needs to be sold. We upload a photo of the dress itself, adjust the simplest parameters, and get a cool angle with a perfect fit on a model that no one photographed or changed. After this, all that remains is to add a couple of phrases for the info graphics, and the card will be ready:
BMore often than not, there is a work pattern that designers call a “five-minute draft.” Instead of spending hours manually assembling the first version of the card, a specialist receives a finished layout almost instantly. Of course, this version is rarely sent to the client without changes. But it is this that becomes the starting point, which is much easier to improve than to create a project from scratch. As a result, the designer begins to work not ten or twenty percent faster, but several times more productive. This is especially important for those specialists who simultaneously run several stores or collaborate with large agencies.
What tasks do neural networks already perform independently, and where can they not yet be done without a human?
KWhen people talk about the possibilities of artificial intelligence, they often get the impression that a neural network can completely replace a designer. In practice, the situation looks more interesting. Modern AI tools have really learned to cope well with a large number of repetitive operations, and everything that can be described by clear rules and algorithms is automated very quickly. That is why today the creation of cards for marketplaces through a neural network is becoming a standard practice for thousands of sellers.
EArtificial intelligence also works great where speed and scaling are required. Neural networks easily generate headlines and SEO descriptions, select keywords, compose product characteristics, create template infographics, remove backgrounds, perform basic image retouching and adapt ready-made cards to the technical requirements of different sites. Some services can automatically create dozens of card options for an entire product line at once or prepare images for mass upload via API and CSV. All this almost completely relieves specialists from the most monotonous part of the work and allows them to focus on tasks that really require professional thinking.
And these are the areas where artificial intelligence remains only an assistant. For example, he does not yet know how to deeply understand brand positioning, the characteristics of a specific audience, or the emotional reasons why a person chooses one product over another. That is why the concept of visual presentation, development of a corporate identity, creation of truly unusual infographics and the search for a strong marketing idea still remain the responsibility of a person. You can ask a neural network to generate an AI picture or even generate an AI video for a product presentation, but only a living, thinking specialist can determine which of these ideas will better solve the brand’s problem.
AndThis is why most professional designers no longer perceive artificial intelligence as a competitor. Quite the contrary, the neural network is becoming another tool, as familiar as Photoshop or Figma once became. It does not replace experience, sense of taste and understanding of marketing, but it removes a huge amount of repetitive work that used to take up most of the working day. And the better a specialist knows how to work together with AI, the higher his value in the market becomes.
How much time do neural networks really save: real numbers and cases
GThe main question that both sellers and designers are asking today is very simple: how much does all this really speed up work? If you look at real cases of agencies that have already implemented artificial intelligence in card production, it becomes clear that we are not talking about minor optimization, but about a serious change in the entire economics of the process.
NFor example, the Bboxa agency talked about the introduction of AI tools into the work of designers, thanks to which the time for preparing one card was reduced from about six hours to thirty minutes. Of course, such a result is not always achieved, but the fact itself shows how much automation changes the usual workflow.
"We upload a photo of the product and its name into the system. The AI independently analyzes reviews, determines the target audience and its key needs. The main thing is that the neural network for marketplaces provides information in the order in which it needs to be placed on the product card" - shared one of the key changes was Vlad Polkin, managing partner of the Bboxa agency.
EThere are also larger-scale examples. Some studios working with large catalogs use artificial intelligence to mass produce visuals of up to several thousand cards in just a few days. Previously, such a volume would have required the participation of a large team of designers over several months.
PAt the same time, it is important to understand that the effect depends on which tasks are automated. If you use artificial intelligence for texts and product descriptions and nothing else, the time gain will be relatively small. If you connect image generation, automatic photo processing, and infographic preparation, the acceleration can reach three to five times, and at some stages even tenfold. That is why today most experts no longer argue about whether neural networks are needed for marketplaces. Another question becomes much more important: what processes should be automated first?to get maximum effect without loss of quality.
DFor designers themselves, this means the ability to take on more projects and pay much more attention to the creative side of the work. For sellers, the advantages are expressed in other ways:
toart cards begin to be created faster,
withthe cost of content production is gradually decreasing,
hThe launch of new products ceases to be a long and expensive process.
AndIt is at this stage of market development that specialized platforms of a new generation appear, which combine several AI tools in one service and allow you to automate almost the entire cycle of creating cards. One of these platforms was Aidentika, which we will talk about in more detail in the next chapter.
Aidentika is a neural network for new generation marketplaces: how does it differ from ChatGPT, Midjourney and other AI tools
KWhen designers first started using artificial intelligence in their work, most processes still had to be assembled literally piece by piece. To write texts, ChatGPT was opened, to generate images - Midjourney or Flux, a separate service was used to remove the background, then the finished image was transferred to Photoshop, where infographics were added and the dimensions were adjusted to the requirements of Wildberries or Ozon.
As a result, instead of one program, the specialist used five or six different tools. This did speed up the work compared to a completely manual process, but it was difficult to call this approach convenient. Time was saved on individual operations, but switching between services and the subsequent assembly of the final card still took up a significant part of the working day.
AndThis is why the next stage of market development was professional platforms, which were created not to generate beautiful pictures in general, but exclusively for e-commerce tasks. Their main idea is to free the seller or designer from having to understand dozens of separate AI services. Instead, we got one tool that already understands the specifics of marketplaces, knows what a product sales card should look like, and automates most of the production process. One of the most interesting representatives of this direction was Aidentika is a service that actually turns an ordinary photograph of a product into a ready-made selling visual in just a few steps.
LThe logic of the service is as close as possible to the way the seller himself thinks. Instead of complex prompts and dozens of technical settings, the user simply uploads a photo of the product, selects the appropriate shooting concept, and then receives ready-made design options.
CThe service can automatically create studio photographs, place a product in an interior, show it on a model, generate product compositions and, in almost one click, add infographics with advantages, characteristics and other elements that usually have to be assembled manually. Moreover, all these actions take place within one platform, without the need to export images between different programs.
OhThe approach to catalog scaling deserves special attention. If previously the designer created each card almost from scratch, then Aidentika allows you to maintain a single visual style for the entire product line at once.
EThis is especially important for brands that sell dozens or hundreds of SKUs. It is enough to define the design concept once, after which the service helps maintain the same composition, style and visual language between all cards. For the buyer, this looks like the work of a professional design studio, but for the seller himself it means significant savings in time and budget. In addition, the service supports the generation of several images for one card at once and allows you to quickly obtain various design options for subsequent A/B testing.
Eone more feature Identities is that it gradually goes beyond conventional image generation. In addition to creating cards, the service offers AI fitting of clothes and accessories on models.
It also automatically creates subject scenes, and can also generate short videos showing a scenario for using a product.
EIf previously, in order to generate an AI picture and separately generate an AI video, it was necessary to use different platforms, now such tasks can be solved within one workspace. This is especially useful for merchants who are simultaneously developing product cards, advertising campaigns, and social media content.
PIt is important to understand that Aidentika is not trying to replace a professional designer. Rather, it changes the very structure of his work. If previously a significant part of the working time was spent on cutting out objects, selecting backgrounds, creating subject scenes, searching for models and preparing dozens of similar options, now all these processes are automated. The freed up hours can be used to study competitors, develop concepts, search for new visual solutions and test hypotheses. These are the tasks that have the greatest impact on sales, but they are the ones that designers most often did not have enough time for in the past.
DFor sellers, the benefits look a little different. They no longer have to order expensive product photography, look for a studio, hire models, or wait several weeks for the designer to prepare a full set of cards. Even a product photograph taken on a smartphone can become the basis for a professional visual. This significantly reduces the cost of launching new products and allows you to quickly launch your product range on marketplaces. Such savings become especially noticeable for sellers with large catalogs, where creating each new card in the traditional way turns into a serious expense item.
We calculated exactly what neural networks help sellers save on
What a person does, what universal neural networks can do and what Aidentika automates
The main conclusion is that neural networks for marketplaces are no longer Iare simply a fashionable addition to Photoshop. They become a full-fledged production infrastructure that allows you to produce high-quality visual content much faster. Universal models like ChatGPT or Midjourney remain excellent assistants for solving specific problems. But when it comes specifically to the mass creation of cards for marketplaces through a neural network, specialized platforms get an advantage. They relieve the user of the need to assemble a workflow from a dozen different services and allow them to focus on what really brings sales - a strong idea, high-quality visuals and constant testing of new hypotheses.
How to implement neural networks today: step-by-step instructions for sellers
PAfter becoming acquainted with the capabilities of artificial intelligence, many make the same mistake. They start looking for the “best neural network,” hoping that it will solve all the problems itself. In practice, such a tool does not yet exist. The maximum results are obtained by those teams that do not replace people with artificial intelligence, but rebuild the process of creating cards. That is why it is better to start implementing AI not with choosing a service, but with revising the workflow.
Pfirst step: collect a high-quality product passport. Even the most modern neural network for creating cards for marketplaces will not be able to come up with product advantages if they are not in the source data.
PPrepare specifications, dimensions, materials, photographs, customer reviews, answers to frequently asked questions and a list of product strengths. The more detailed the initial information, the better the result. Artificial intelligence can quickly analyze data, but is not able to replace the knowledge of the seller himself about his own product.
Second step: choose the right set of tools. If previously a designer had to open five or six different services, today the process can be simplified. A large language model helps you quickly prepare headlines, SEO descriptions, and text for infographics. The image generator allows you to create custom scenes or test several options for presenting a product. But it is possible to implement one professional neural network for marketplace cards, which combines most of these processes in one workspace and eliminates the need to constantly switch between different programs. And it’s even cheaper than several different neurons.
Trust, but verify! Determine quality control rules in advance. Artificial intelligence is, of course, capable of creating dozens of card options in a few minutes, but sometimes it can make mistakes. Therefore, the final check should always be performed by a human. Assign someone who will check:
Is the infographic easy to read?
does the image correspond to the brand positioning,
whether the emphasis is placed correctly and whether the card is not overloaded with unnecessary information.
XA good practice is to give a mockup to someone who has not seen the product before. If he understands the main advantages of the product in a few seconds, then the card fulfills its purpose.
Third step: stop relying only on your own opinion and start testing hypotheses. Marketplaces allow you to quickly understand which visual works better. Make two versions of the card: one more minimalistic, the other with rich infographics and additional benefits. Then conduct A/B testing and compare CTR, card view depth, add to cart, and final purchase conversion. This is the approach that large brands use, because even a small increase in CTR by a few percent can lead to a noticeable increase in sales with large volumes of traffic.
Fourth step: If the range is constantly expanding, then the next stage is the automation of mass production of cards. Instead of creating each image separately, it is better to immediately develop a single visual style, templates and design rules for the entire product line. Right here AI services for marketplaces show maximum efficiency. They help you quickly scale your catalog while maintaining a consistent design and corporate identity regardless of the number of SKUs.
GThe main expectation from the introduction of artificial intelligence must be realistic. The goal is not to eliminate designers' work entirely, but to eliminate the slowest and most repetitive processes. In most cases, companies receive three to five times faster card production, and even more at certain stages. The team begins to use the freed up time not for mechanical image processing, but for analyzing competitors, searching for new ideas and constantly improving visuals. This is what ultimately leads to increased sales.
Why Aidentika is especially useful when the catalog begins to grow rapidly
EIf you have five products, then creating cards manually can still be considered a working scheme. But when the assortment grows to several tens or hundreds of SKUs, the usual process begins to literally slow down business development. While the designer is preparing new images, the competitor is already introducing the next products to the market, testing new hypotheses and collecting data on which cards perform better. That is why today more and more sellers are considering automation not as a way to save money, but as an opportunity to scale their business faster.
In such a situation Aidentika becomes not just another neural network, but a full-fledged production platform. It helps automate the creation of visuals, the preparation of infographics, the generation of subject scenes, trying on products on models and the creation of several versions of cards for testing. Instead of a long chain of several programs, the user receives a single workflow, which makes it possible to release new content and update existing cards much faster. And it is suitable for absolutely any niche - from auto parts to dog food.
PAt the same time, it is important to understand where the limit of artificial intelligence capabilities lies. Aidentika copes well with repetitive operations, speeds up content production and helps scale the catalog. But she doesn't make strategic decisions for the business. What visual style will distinguish the brand from competitors, what emotion should the card evoke and what unique selling proposition will become the main argument for the buyer - all this remains a human task. That is why the best results are achieved when the experience of a seller or designer is complemented by the speed of modern AI technologies.
EIf you are just starting to implement artificial intelligence in your work, you should not immediately transfer your entire catalog to a new system. It is much more effective to conduct a small experiment. Select ten products, prepare cards for them using Authentic, test them for two weeks and compare the performance with existing images. Pay attention to CTR, conversion to order, number of additions to cart and speed of preparing new cards. Such a test will provide much more useful information than any reviews or advertising promises.
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Register follow the link on the Aidentika website and get free generation
And after the first test you will receive an individual promotional code for a 10% discount
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Checklist: are you ready to speed up card creation by 5 times?
✔ Ccollect a complete product passport before starting work.
✔ ANDuse AI for text, images and routine processing.
✔ TTest at least two versions of each card.
✔ Mscale your catalog through uniform templates and automation.
✔ Aanalyze CTR and conversion, not just the beauty of the design.
Try preparing the first 10 cards using Aidentika and compare the result with your current process. In just a couple of weeks, it will become clear how much time was saved, how the speed of the team’s work has changed, and which cards attract customers better. It is this kind of practical test that will allow you to objectively assess how AI can accelerate the development of your store on marketplaces.
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