Would you shop from a feed of AI-generated images?
The startup Dalet Labs is betting you will.
The AI-commerce company exclusively told CMO Insider that it raised a $7 million pre-seed round led by Lightspeed Venture Partners and is launching its first product, a shopping app called Scene.
Founded by Omer Bar-Tal in 2025 after he left the AI video startup Runway, Dalet Labs recently came out of stealth mode. The startup raised its pre-seed in late 2025, before finalizing its product road map. In addition to Lightspeed, venture capital firm SV Angel and individual angel investors participated in the round.
Lisa Han, the Lightspeed partner who led the round, said that Bar-Tal has a combination of “video AI research expertise” and a “deep understanding of what consumers want.”
Scene, which became available on the Apple App Store in the US on Wednesday, lets people scroll through a feed of shoppable AI-generated images. The app will be invite-only at launch, and isn’t yet available on Android. While the images of people or scenes are AI-generated, the products themselves come from real brands across categories like fashion, home decor, and sports.
Bar-Tal envisions Scene as a tool people can turn to when they want to buy something but are still uncertain about exactly what they have in mind. It’s similar to scrolling through Pinterest or TikTok in hopes of being inspired to buy something, but Bar-Tal said there’s a key difference: You come to Scene to shop, not be entertained.
Bar-Tal sees Scene as a virtual “mall” where users can window-shop, save items to their cart, and make purchases.
Commerce has become an increasingly attractive use case for AI tools like chatbots and agents (including Meta’s Muse). However, Bar-Tal argues that AI shopping tools are missing something.
“AI is amazing when you know what you want, but it’s really important to actually have an ability to discover and to see things,” Bar-Tal said. Agentic commerce, he added, “misses the point in shopping — which is essentially a delightful experience.”
As a Scene user scrolls and interacts with products linked in the app, the feed adapts to what type of brands or items the user is interested in. Then, Scene uses AI to generate scenes with people wearing or using those items.
Bar-Tal did not comment on which third-party models Dalet Labs uses.
Bar-Tal said Dalet Labs doesn’t see its product as replacing influencers, but rather as “complementary.”
“The content here is AI, but it is not aiming to replace creators because we are not aiming to create entertainment,” he said.
However, even if Scene isn’t competing with influencers for entertaining content, it’s still competing with their business model: making money by promoting brands’ products and ultimately driving sales.
Scene has a catalog of hundreds of brands and retailers, Bar-Tal said, and is making more partnerships. The company did not share further details on what brands Dalet Labs has already established relationships with.
Bar-Tal said the company is also leveraging affiliate marketing to monetize the app early on, working with affiliate networks to earn from each in-app transaction.
Dalet Labs’ Scene is entering a crowded market of apps offering people ways to discover brands and goods, like TikTok’s growing Shop presence in the US or Pinterest’s visual search prowess. Startups like Doji and Alta have also raised millions on the promise of tech that lets people try on clothes using AI-generated versions of themselves.
How will Dalet Labs get people to download an entirely new app?
Using influencers and social media to promote the app will be a core part of Dalet Labs’ user acquisition plan. It also plans to roll out a coupon campaign during the holidays, offering new users discounts on purchases made through the Scene app. Bar-Tal didn’t share whether the discounts would be funded by Scene or by brand partners.
The New York-based startup, meanwhile, is using its pre-seed funding to hire more engineers and researchers to build out Scene.
Read the 5-page pitch deck Dalet Labs used to raise its $7 million pre-seed:
The deck starts with background on the technology
Here’s what the slide says:
For over half a century, every major shift in computing has been driven by an interface revolution.
The move from terminals to graphical user interfaces (GUIs) transformed computers from expert tools into mass-market products.
The rise of the web and mobile extended that pattern: each new medium made digital interaction more visual, intuitive, and consumable.
Text, by nature, demands active cognitive effort; Visual interfaces go a step further by allowing users to grasp high bandwidth information almost instantly with minimal cognitive effort.
Then it explains Dalet Labs’ thesis
Here’s what the slide says:
Today, artificial intelligence represents the most profound computing shift since the PC. Yet, paradoxically, our interface to AI remains stuck in the terminal era.
We converse with models through text prompts and receive text streams in return in synchronous manner. Our product thesis is simple and inevitable: AI requires a native GUI.
We believe the GUI to AI will not be code-based, but rather generated by media models.
It then outlines the competition
Here’s what the slide says:
The core product of AI consumption platforms is inseparable from their textual chat interfaces.
For example, the OpenAI brand is strictly associated with the chat interface of ChatGPT.
Moreover, by decoupling the user interface from any specific model provider, we can integrate multiple frontier models (OpenAI, Anthropic, Google, or open-source systems) and focus our innovation on the new media generation GUI platform. This modularity becomes increasingly valuable as AI models are commoditized. In addition, their technical stacks are anchored in the code generation paradigm, in contrast to our focus on Media Models.
Finally, it introduces Dalet Labs’ founder
Here’s what the slide says:
Omer Bar-Tal
-
Research experience from Weizmann Institute.
- Built the first method for editing real-world videos using text
- Papers in CVPR, ECCV, ICML, ICLR etc.
- Trained video foundation models (Google, Pika, Runway)
- Built creative applications with media models (eg video editing, reference-to-video, etc.)
- Led applied research teams and product launches
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