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Generative AI in fashion: A conversation with Michael Musandu, on  tech, diversity, and sustainable fashion

startups And Financing

Generative AI in fashion: A conversation with Michael Musandu, on tech, diversity, and sustainable fashion

1621374593317

Bianca Iulia Simion

· 5 min read
Latest posts
startups And Financing

Generative AI in fashion: A conversation with Michael Musandu, on tech, diversity, and sustainable fashion

1621374593317

Bianca Iulia Simion

· 5 min read

Michael Musandu, Forbes Europe 30 under 30, is the CEO and Founder at Lalaland, an Amsterdam-based fashion-tech startup that leverages generative AI to create hyper-real fashion models for brands, supporting from the initial digital product creation process to consumer-facing sales enhancements. Lalaland won the Tommy Hilfiger Fashion Frontier Challenge in 2022 and received investment from Google for its European “Google Black Founders Fund in June 2021.”

1. What inspired you to create Lalaland? What drives you?

Lalaland was founded to increase the number of models across countless characteristics without being restricted by production costs, providing a pathway so that all customer segments feel represented online.

I'm deeply motivated by opportunities brought to life when diverse founding teams create disruptive technology through social empowerment and sustainable impact.

Additionally, I'm motivated to play a pivotal role in building networks to increase opportunities for future generations of black and minority founders. This includes sharing access to social and financial capital and developing the networks required to ensure that entrepreneurs are as diverse as the markets we serve.

2. How does your background in computer science and AI influence your approach to diversity in the fashion industry?

I moved to the Netherlands where I pursued a degree in computer science and later specialized in artificial intelligence. I felt this massive problem of people feeling underrepresented while shopping online. My (now business) partner Ugnius Rimsa felt the same. As he had a background in fashion, together we explored why brands struggled to showcase this inclusivity and why online shoppers couldn't easily find models who looked like them. This collaborative effort led to the creation of Lalaland. It was all about solving the problem of people feeling underrepresented online. To see someone in your size, with your shade, to give you a better indication of whether that clothing item fits you or not. Later on, we realized that this is something that should be tackled head-on; during the digital product creation process, starting with designers before ending up on e-commerce.

3. Lalaland is known for its personalized models. How do you see this innovation impacting the fashion eCommerce landscape, and what challenges did you face in its development?

One of our main aims was to create models that genuinely mirror our society while also fitting the aesthetics of the fashion industry. Here's the challenge: the internet often lacks diversity and is riddled with biases and poor representation.

So, our overarching vision revolves around addressing this gap and making it more representative. That's why we embarked on a journey to broaden our data sources and foster collaborations through data purchase initiatives of underrepresented BIPOC communities. This allowed them to benefit financially and contribute to our dataset, forming the very foundation of our algorithmic processing. Our ongoing commitment involves maintaining strong partnerships with these minority groups, with data procurement remaining a crucial aspect. This approach ensures compliance with regulations like GDPR and CCPA, underscoring our commitment to ethics. Think of it as a kind of rigorous digital casting process.

We're not just aiming for an accurate reflection of society but also ensuring our models have that distinctive 'fashion look,' aligning with industry trends like makeup, lighting, and other subtle factors. Our creative director (Duy Quoc Vo) with 15 years of experience in the fashion industry as a fashion photographer, plays a pivotal role in achieving this look and feel.

4. How does Lalaland's technology reduce return rates, and what impact does it have on customer satisfaction and environmental sustainability in the fashion industry?

One of the core reasons we embarked on the path of 3D and computer-aided design was a realization that designers heavily rely on physical samples to ensure their designs fit, look good, and adhere to conventional sizing (S, M, L, XL), among other factors. With Lalaland’s plugin, designers can now have the ability to digitally visualize, review, and iterate on their garments before any physical production takes place. This digital approach significantly reduces the need for physical samples. Moreover, the photorealistic quality of our product has allowed clients to use the final images for their e-commerce platforms. This not only speeds up the process but also facilitates practices like 'made to measure,' where products are showcased to customers before actual production. Once a purchase is made, the item is then manufactured, minimizing wastage and so on. Additionally, brands can now gauge consumer sentiment by testing various products through online showcases before initiating production. This rapid feedback loop harnesses market validation and incorporates emerging trends promptly into the production process.

5. Lalaland collaborates with brands like Patagonia and Levi’s. At what scale are they using the solution? What value do Lalaland's models bring to them?

It’s not only global brands working with our models. With over 128 brands on our platform, our AI models also help to create equity for brands of all sizes / different budgets, allowing them to scale their photoshoots, and in some cases show their products on models at all.

6. Lalaland's technology helps brands attract new customers. Can you share instances where it positively impacted a brand's customer engagement and market reach?

Due to our involvement starting early in the value chain, spanning from product development to go-to-market stages, we've achieved a remarkable turnaround. On average, designers require only 5 to 30 minutes to design a garment and place it on a model image.

Quicker and more efficient doesn’t mean guaranteed sales or better. The speed and efficiency offered by AI-generated models are valuable, but we understand that it's not just about that; it's about creating marketing materials that are authentic and brand-specific to drive sales. That's why our involvement begins at an early stage in the value chain. We provide the tools for immediate visual representation, allowing designers to see, refine, and make adjustments to their 3D garments swiftly.

This rapid visualization of designs offers brands the advantage of having more time and resources at their disposal to respond promptly to market trends. They can focus on incorporating authentic storytelling into their campaigns, utilizing real models, and crafting compelling narrative dialogues. In essence, our approach doesn't just streamline the creative process; it empowers fashion brands to authentically engage with market trends and deliver content that resonates with their audience, all while maintaining their brand's unique identity.

7. As the CEO, how do you envision Lalaland's future growth? Can you share insight into upcoming projects, partnerships, or technological advancements?

Lalaland will be introducing its male population this quarter alongside an additional integration to Clo3D, giving us access to over 1k news brands like H&M, Hugo Boss, and Marks & Spencer to mention a few.

As we move forward, we will continue to collaborate with our partners and actively seek feedback from users to guarantee that our AI models remain a positive transformative force within the fashion industry.

Looking at the training data, Lalaland's AI models also provide a new revenue stream for BiPOC communities creating an earning structure on the input data utilized in training our algorithms. While our AI-generated models are created from scratch, we can achieve hyper-realistic quality because we train our algorithms on actual photos.

8. How does Lalaland stay ahead of fashion trends and adapt its technology to meet evolving brand and consumer needs?

The fashion industry has often been critiqued for perpetuating rigid and exclusive beauty standards throughout its history. We've witnessed these narrow definitions of beauty persist, leading to the exclusion of certain groups from the industry's narrative. However, the current landscape is undergoing a significant transformation as the world increasingly transitions into a digital realm where digital avatars and twins are becoming more commonplace, gradually establishing a new norm.

It's crucial to recognize that AI is a tool that follows the guidance of those who develop and utilize it. This dynamic can present challenges, such as machine bias. However, when it comes to reshaping beauty standards, real change occurs when both our culture and the individuals shaping technology are committed to pushing for inclusivity and creating images that authentically reflect society.

What adds an interesting dimension to this landscape is the timing of AI's emergence coinciding with Gen Z (and younger generations) breaking down traditional beauty norms. As these generations mature and enter the (tech) working field, we hope that they will bring their progressive mentality into it. At Lalaland, we view our involvement in this process as a profound responsibility. Our team is also quite young and encompasses diverse backgrounds, offering cultural understanding and a strong commitment to redefining beauty norms. Looking at our AI models, they’re as real as possible. From a company perspective, our focus on the 3D realm marked a significant milestone for us. This brought us closer to our goal of reshaping the narrative and empowers creators to consider and care about who will be wearing their garments right from the inception of the design process. Our tool enables them to visualize their 3D garments on our AI models quickly, typically within 5-30 minutes. This eliminates the need for physical samples and encourages designers to think about how their creations fit different body sizes and complement various skin shades. Beyond its efficiency and sustainability benefits, we hope that our tool fosters a more inclusive mindset when it comes to creating designs through the use of AI.

9. Operating internationally, how do you navigate challenges and opportunities in the global market?

Due to the geopolitical situation, there are supply chain challenges like getting physical samples from East Asia to Europe or the USA, and the macroeconomic climate, brands are focusing on cost-saving measures. This has led to hyper-growth for both CLO3D, Browzwear, and other CAD tools presenting an awesome opportunity for Lalaland.

Operating internationally as a B2B SaaS platform, our strategic approach is tailored to effectively navigate the complexities and capitalize on the opportunities of the global market. Central to our strategy is the localization of generated models, ensuring they resonate with various ethnicities and cultural contexts. This commitment to relevance and sensitivity is complemented by our adherence to international regulations like GDPR and CCPA, and we're proactive in developing our own AI principles in anticipation of evolving standards such as the EU AI Act.

Our platform's infrastructure is designed for high scalability, both technically and operationally. This design choice enables us to manage increasing user loads and expand into new markets seamlessly, ensuring a consistent and reliable user experience. Integral to our service is the integration of global payment systems, facilitating smooth and secure transactions for clients worldwide.

Moreover, we place a strong emphasis on robust customer service. Our support teams are equipped to handle a diverse range of inquiries and challenges, ensuring that client needs are met promptly and efficiently. This comprehensive approach, combining technological adaptability with a deep understanding of regulatory and cultural nuances, positions us as a reliable and forward-thinking partner in the international B2B SaaS landscape.

10. What are the exit scenarios for investors backing Lalaland?

Our commitment is continuing to grow our ARR to €450K by July 2024 to become EBITDA positive in 2024.

Investors backing Lalaland have several potential exit scenarios, particularly in light of recent industry trends:

  1. Acquisition by a Customer/Potential Customer (Fashion Brand): Fashion brands, as customers or potential customers of Lalaland, might acquire the company to directly integrate its technology. This vertical integration could offer them competitive advantages and operational efficiencies.
  2. Acquisition by Large Competitors (e.g., Traditional Photography, AI Models, Vue.ai): Competitors may acquire Lalaland to eliminate competition, expand their market share, or incorporate Lalaland’s technology into their offerings. This follows a trend of larger players consolidating emerging technologies to strengthen their positions.
  3. Acquisition by Companies with New Product Offerings (e.g., Browzwear, Clo3D, Style3D, Adobe Substance): These companies might see Lalaland as a strategic acquisition to diversify their product lines and capitalize on their existing customer bases.

Considering the recent acquisitions in the industry—Zeekit by Walmart, Allure Systems by Farfetch, Presize.ai by Meta, and Metail by Trong—it's evident that there's significant interest in innovative fashion technology solutions, providing diverse exit opportunities for Lalaland's investors.

11. Why should investors join this opportunity?

Timing, you are investing in the current market leader when it comes to generative AI fashion models. Lalaland has a first-mover advantage with priority algorithms and its unique training data, which adds to the core defensibility of the technology. It is not a wrapper built on top of something but rather venture-scale technology that has just begun its initial distribution strategies as its GTM strategy.

High product stickiness and high switching costs for any brand using CAD for digital product creation.Consumer data stream, deep understanding of which type of models to show to individual consumers to capture a conversion moment.

We’re servicing our current sales funnel and warm pipeline (i.e., qualified leads) with potential of >€5M ARR that will reach profitability in 2024, with positive EBITA

Remarkable validation for the adoption of AI avatars in the B2B sector in 2023, particularly as it involves Fortune 100 companies. (Large enterprises typically exhibit a slower pace in embracing new technology.)

Hence, it is only a matter of time before this trend permeates the fashion industry, providing us with another solid justification for the relevance of "why now".

The increasing utilization of AI avatars across various domains is beneficial as it fosters familiarity and acceptance of digital personas among a broader audience.

Explore the opportunity on SeedBlink.

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