If content is king, Savanah.ai wants to be its AI queen.
The AI visual operating system helps fashion brands create visual content across the entire product lifecycle, from early design and product visualization to product detail pages (PDP), editorial, marketing and video. Here, Hertzman Global Intelligence chats with Zehra Soysal, CEO of Savanah.ai, about how AI is changing the possibilities and economics of producing images.
HGI: How does Savanah fit into a brand’s existing content-production process, and what kind of efficiencies can brands expect?
Zehra Soysal: Brands can start with a product image, sketch or existing asset and generate on-model imagery, flat lays, ghost mannequin images, editorial campaigns, video, technical sketches and tech packs. For larger brands, we can also define their visual “brand DNA.” Things like lighting, composition, model profile, styling and color palette so content can be generated consistently across hundreds or thousands of products. We’re increasingly working with brands not just on individual images, but on automating visual production across entire collections and channels.
Depending on the workflow, we typically see 60–90% cost savings compared with traditional production. A conventional fashion shoot involves models, photographers, stylists, studio or location costs, samples, logistics, retouching and post-production. From planning through final delivery, that process can easily take a couple of weeks. With Savanah, the same type of production can often be completed in a few days.
HGI: How does the content-production model change when brands can move beyond shooting products one at a time to generate visual assets across an entire collection?
ZS: The difference becomes even more significant at scale. For example, if a brand needs hundreds or thousands of PDP images, it no longer has to physically shoot every product in every pose or format. Once we define the brand’s visual requirements, we can produce repeatable flat lays, ghost mannequin images, on-model content or editorial assets across the collection. So the benefit isn’t only reducing the cost of an individual shoot. It is turning visual production from a relatively slow, expensive process into something software-based, scalable and repeatable across hundreds or thousands of products.
HGI: A beautiful image has very little value if the garment is represented incorrectly. How do you make sure the product still looks exactly right?
ZS: Product accuracy is probably the most important technical challenge in fashion AI. We use the original product assets as the source of truth and build workflows designed to preserve silhouette, construction, color, prints, logos, fabric characteristics and other product details. For enterprise clients, we can also configure the system around their own photography standards, required poses and visual guidelines.
But human review still matters. We don’t believe AI should remove creative or quality control; it should remove much of the repetitive production around it. One of the biggest advantages is that the economics of content creation change completely. Instead of deciding whether it is worth paying for another shoot to get three additional poses, another model or a short video, brands can generate those variations very quickly. That means more angles, more styling, more editorial options, more video and more content tailored to individual channels.
HGI: As Savanah can create different models, body types and demographics, have you seen this changing the way brands approach inclusivity?
ZS: Historically, representation has also been constrained by production economics. Every additional model, body type or localized campaign requires more casting, another shoot and more production budget. AI removes much of that constraint. With our platform, a brand can present the same product across different models, body types and markets while maintaining the same underlying brand identity.
But I think the bigger long-term story is personalization. Instead of every consumer seeing the exact same model and styling, visual content can eventually adapt to different audiences, geographies or customer profiles. We call that idea a Dynamic Storefront: the product stays the same, but its visual presentation can change depending on whom the brand is speaking to.
HGI: What other parts of the fashion business will technology tackle next?
ZS: The next major transformation is connecting design, visual production and commerce into one continuous system. Today those stages are still very disconnected. A designer develops a product, physical samples are made, photography happens later, the product launches, and only then does the brand receive meaningful customer feedback.
AI can compress that entire feedback loop. Brands can visualize products before they physically exist, explore different colors and materials, create campaign concepts, test different visual presentations and eventually use performance data to inform both creative and product decisions.
That is why we describe Savanah as a visual operating system rather than simply an image generator. The interesting opportunity is not generating one picture faster; it is changing how visual decisions are made throughout the entire fashion lifecycle.
HGI: What can AI teach a brand about customer behavior before items ever go into production? Is it tailor-made to drive an on-demand model?
ZS: I think this is one of the most transformative possibilities. Instead of committing to a product and then discovering months later whether consumers respond to it, a brand could create realistic visualizations of different silhouettes, colors, prints or styling directions before production.
You can then test those variations, understand what consumers respond to and feed that
information back into design, merchandising and production decisions. The same applies after launch. Instead of having one fixed “hero shot, ” brands can generate many visual variations and learn which performs best for each SKU, audience and channel. We are already moving from a world of static imagery toward visual systems that can be continuously optimized. Ultimately, that does support a much more demand-driven model: test earlier, manufacture more intelligently and potentially reduce unnecessary inventory.
HGI: What do such AI advances mean for the traditional fashion photography and production business, and even the consumer?
ZS: I don’t think photography disappears. I think the role of photography, and creative teams more broadly changes. A large part of fashion production today is repetitive: thousands of PDP images, standardized poses, localization, additional colorways and channel-specific assets. I expect a significant share of that everyday, high-volume production to become digital. But physical photography still creates something unique: spontaneity, human energy, cultural relevance and serendipity. Major campaigns, brand storytelling and important creative moments will continue to have a place. I think the future is much more likely to be hybrid than purely AI-generated.The roles of creative directors, photographers and stylists also become more about vision and direction rather than production mechanics. AI can amplify creative judgment, but it doesn’t replace taste.
As for consumers, I think the question increasingly becomes less “Was this made with AI?” and more “Is this accurate, authentic to the brand, and does it make me feel something?” Transparency matters, but the production technology itself becomes secondary if the product is represented accurately and the visual feels authentic.
HGI: Looking five years ahead, what part of today’s fashion-content business do you think will look completely different because of AI?
ZS: I think the idea that a brand creates one fixed set of images for a product will feel outdated. Today, a product might have five or 10 images that are photographed once and then shown to millions of consumers. Five years from now, visual content will be much more dynamic. The same product could have different imagery depending on the customer, geography, channel, campaign, season or context. Brands may continuously generate and optimize creative rather than organizing a completely new production every time they need another asset.
Creative teams will also shift from asking, “How should we shoot this product?” to “What visual system should we build around this product?” That shift from individual assets to intelligent visual systems is what I think will fundamentally reshape fashion content. And importantly, lower production costs don’t necessarily mean less creativity. They can mean more experimentation. When trying a new visual direction no longer requires committing the budget of an entire shoot, brands can afford to take more creative risks, test them and learn.
