How AI Agents Could Rebuild Fashion’s Visual Production Layer

A fashion campaign rarely begins with a single creative idea. It often begins with a mix of garment data, sample logistics, approvals, handoffs, retouching notes, asset management and a long queue of people moving work between systems. Three pioneering companies in the AI space, Genera, OmegaRender and AlphaRender, argue that much of that operational layer can be absorbed into software, and then into something more ambitious still: agent systems that coordinate production logic across the visual stack. When speaking with them, the trio described “a new technological layer” taking shape inside the visual economy, one built not just to assist visual production, but to run more of the machinery that once lived inside studios.

That is an ambitious proposition that needs to be handled carefully, but it does land in a market that is already moving in the same direction. WPP’s Production Studio, developed with Nvidia Omniverse, is pitched as an “AI-enabled, end-to-end production application” that addresses the challenge of producing “brand-compliant and product-accurate content at scale,” with “human oversight… at every stage of the workflow.” Adobe’s Firefly Creative Production promises “repeatable content pipelines” and says its aim is to make creative production “operational rather than experimental,” while Adobe’s Runway partnership is framed around “the next generation of AI-powered video workflows” inside tools creators and brands already trust.

Interestingly, Genera’s collaboration comes at this from the other direction. Instead of taking a large software platform and extending it into workflows, the group is trying to turn a decade of studio production knowledge into a new operational layer.

When Studio Expertise Becomes Visual Production Logic

The foundation here is OmegaRender’s expertise. The studio is presented as the visual base layer for the wider group, built through years of high-end production work across architecture, entertainment, gaming and large-scale digital environments. The argument is that OmegaRender accumulated something more useful than a portfolio: an understanding of how complex visual production actually works, at the level of coordination, iteration and decision loops. That knowledge then fed into AlphaRender (interactive concept design for architects and designers) and Genera as software products, and now feeds into the next step, which the group describes as “agent infrastructure”.

Oleksii Fedorenko, R&D at Genera, puts the shift in structural rather than aesthetic terms. “The fundamental shift is not about better algorithms or faster rendering models. The real shift is structural.” He continues: “Instead of humans operating software, intelligent systems begin operating the software themselves. That changes the architecture of work.” Fedorenko says that when the group built Genera and AlphaRender as software platforms, it was effectively encoding “operational logic into technology,” with the next stage being “building agent infrastructure capable of running those systems directly.”

This is a strong claim, but it is also where the idea has the potential to be really meaningful for creative pipelines. For years, the creative industries have talked about software as the tool layer and people as the operating layer. What this group is proposing is that the operating layer itself becomes software. Aleksandr Seliverstov, CBDO at OmegaRender and AlphaRender, describes the current enterprise reality in blunt terms: “A typical enterprise pipeline includes dozens of tools: design platforms, asset management systems, analytics platforms, marketing software, production tools… the majority of that work is not conceptual thinking. It’s translating tasks between systems.”

Artem Kupriyaneko, CEO of Genera and founder of AlphaRender and OmegaRender, frames the point even more directly. “Routine intellectual labor” is what gets replaced first, he says, not vision or strategy, but “the operational machinery that executes those processes.” This is of particular importance in industries where so much commercial value depends on moving visual assets quickly and consistently through layers of systems and sign-off.

Why Fashion Is The First Real Test Of AI Visual Production

Fashion is one of the clearest places to test this strategy, because visual production sits so closely to the commercial core of the business. Product pages, lookbooks, campaign assets, wholesale presentations and social content are central to how brands sell, yet they still depend on some of the most coordination-heavy workflows in the industry. Genera describes visual content production as one of fashion’s “most complex and expensive processes,” still tied to photoshoots, logistics, large creative teams and weeks of sequencing.

And it is for this reason that Genera is positioning itself less as an image tool and more as fashion infrastructure. The company says its platform, Genera.Space, generates production-ready imagery directly from garment data and integrates product visualisation, ecommerce, marketing content creation and video production inside one enterprise environment. Their system was developed through more than eighteen months of industry collaboration and over one hundred proof-of-concept projects, and is already being used by brands including The North Face, Vans, Timberland, Ecco, Zalando, J.Lindeberg, Icebreaker and Le Coq Sportif. For some enterprise clients, Genera says the shift has delivered up to 80% cost optimisation across ecommerce and marketing content production, with timelines reduced from weeks to minutes. Those are company-supplied figures, but they speak to the scale of the operational ambition.

The strongest line in their press release states a bold ambition: “Visual content, traditionally the final stage of the fashion cycle, becomes the starting point.” The significance may not be immediately obvious, but if imagery moves upstream, closer to design, merchandising and demand planning, then visual production stops behaving only as a downstream output, but becomes part of how products are developed, tested and commercialised in the first place.

Sofia Polyakova, COO at Genera, links this directly to one of fashion’s deepest structural problems: overproduction. “The current fashion model forces brands to make decisions before they truly understand demand,” she says. “Only after all of that do brands discover what customers actually want.” AI-generated visualisation, in her telling, gives brands a way to “simulate entire product ecosystems before manufacturing anything,” so that “production becomes informed by real signals instead of speculation.” Her final point is the most significant: “The irony is that better visualization technology may actually reduce physical overproduction.”

Fashion is one of the few industries where imagery, demand signals and brand perception are so tightly entangled that better visual systems can influence far more than marketing. They can shape planning, approvals, localisation and inventory decisions too.

The Shift From Creative Tools To Visual Production Systems

This is why the broader market context matters. WPP’s Production Studio was launched with a promise to “unlock exponentially more content,” but the more revealing language in its release sits around supply chains, governance and product accuracy. Production Studio directly addresses the challenge of producing “brand-compliant and product-accurate content at scale,” while maintaining “human oversight… at every stage of the workflow.” Adobe is pushing the same story from a different angle. Firefly Creative Production is built around repeatable workflows, control layers and integrations across the content stack, with outputs designed to stay “synchronized as you scale.”

Adobe’s partnership with Runway says something similar about where creative AI is heading. “Runway’s generative video innovation combined with Adobe’s trusted pro workflows will help creators and brands expand their creative potential and meet the growing demands of modern content and media production,” Adobe CTO Ely Greenfield said when the two companies announced their multi-year deal. This sentence captures the market’s current mood rather well; the focus is no longer only on the model, but on the workflow wrapped around it.

Fashion has its own parallel moves. Browzwear, now incorporating Lalaland’s technology, is pitching “brand-specific, hyper-realistic models” that fit directly into digital workflows for approvals, wholesale, ecommerce and marketing. In other words, the category is steadily moving away from isolated visual tweaks and towards systems that sit deeper inside commercial operations.

With this context in mind, Genera, OmegaRender and AlphaRender are not proposing something completely alien to the market, but are pushing a stronger version of a direction the market is already taking: from tools to workflows, from workflows to operating layers.

The Limits Of Agent Infrastructure In Visual Production

This is also the point where the trio’s ambition becomes hardest to evaluate. In our discussions, the group said the objective is to provide companies with “a new operational layer” and eventually “agent systems capable of operating those platforms autonomously.” Whilst this may prove directionally right, it is still some distance from how most large fashion and creative organisations actually want to work today.

Acknowledging those limits, Daniil Khayrutdinov, R&D at Genera and AlphaRender, says the fear around unpredictable AI systems is “legitimate” and argues that “structure matters more than raw intelligence.” The agent infrastructure being built by the group, is designed with “defined scopes, operational constraints and security layers,” because “companies will only adopt these systems if they are secure, predictable and aligned with real business processes.”

This caution is critical, because fashion brands are particularly sensitive to control, rights, approvals and consistency. As Fedorenko suggested, a system can generate thousands of variations and can monitor performance continuously, as well as coordinating outputs across multiple technologies; but the further these systems move into brand-critical environments, the more scrutiny they will face around provenance, governance, liability and sign-off.

WPP’s official language is particularly pertinent on this subject. Even in one of the most assertive enterprise AI production systems on the market, the company is still foregrounding human oversight and legal compliance. Adobe is doing much the same through its language of visibility, control and synchronized systems. This suggests that the immediate future is unlikely to belong to fully autonomous visual production, but is more likely to belong to layered systems where more of the operational work becomes machine-led while the highest-stakes decisions remain human.

Product And Creativity Return To The Centre

The most intriguing parts of discussions with the three companies came when they stopped talking about automation and started talking about what happens after it. Anton Averich, CTO at AlphaRender and Genera, says the more operational work disappears, the more “time and cognitive bandwidth” return to the product itself. “The paradox of automation is this,” he says. “The more operational work disappears, the more attention returns to human needs and product refinement.” In fashion, he argues, this could mean collections designed around more precise customer insight rather than broad speculation.

Seliverstov made a similar point from the creative side. “Creative industries may actually benefit the most,” he says. “When operational friction disappears, creative teams spend less time coordinating pipelines and more time focusing on ideas. The paradox is that automation of production may actually increase the value of genuine creativity.”

This is where this collaboration has the potential to be most powerful. The case for replacing every layer of studio work with agent systems still has a great deal to prove, but the case for turning more of visual production into infrastructure already feels much stronger. Fashion is especially exposed to that shift because it relies so heavily on imagery, variation, approvals and timing. If those layers can be systematised more effectively, the commercial impact will be felt well beyond content teams.

The real significance of the Genera, OmegaRender and AlphaRender collaboration sits there. It suggests that the next phase of fashion AI and visual production will be more about reorganising the operational architecture behind how images are planned, created and moved through the business, rather than how to produce isolated images themselves. This redefines where the role of a studio begins to live within the process, instead of making them disappear completely.