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Have you ever ordered clothes online, only to realize they don’t fit or look completely different from what you expected? You are not alone. In online fashion retail, up to half of all apparel purchases are returned, mostly because customers cannot accurately visualize how clothes will look on their own bodies. This frustrates shoppers and creates enormous logistical and environmental costs for retailers.

RealClo aims to change that. It is a generative AI powered solution that lets customers see how clothing would realistically fit and appear on their unique body type before buying. The concept combines virtual try-on technology with inclusivity and sustainability, making online fashion more personal, reliable, and responsible.

From Guessing to Confidence

RealClo integrates directly into fashion retailers’ online stores as a cloud-based plug-in. When browsing a product page, customers can click “Try On” to create a personalized avatar that matches their body type. They can adjust features such as height, body shape and skin tone or add measurements for more accuracy. Within seconds, RealClo’s GenAI engine generates a realistic 3D visualization of how the clothing would look when worn.

This will turn online shopping from guesswork into confidence. Shoppers can see how a dress falls on their shoulders or how jeans fit around their hips. Retailers benefit too: when customers buy with confidence, satisfaction increases and return rates drop.

Inclusivity and Real Representation

Most fashion websites still show clothes on one body type. RealClo changes that by allowing avatars that represent a wide range of shapes, sizes and skin tones. Seeing products on bodies similar to their own helps customers feel represented and understood. For brands, this inclusivity strengthens authenticity and builds trust, especially among younger generations who value diversity and transparency.

Scalable and Sustainable by Design 

Behind the scenes, RealClo runs entirely in the cloud, so it works on any device without special hardware. Retailers can integrate it easily through an API. Returns will be significantly reduced: one of the largest sources of CO₂ emissions and waste in online fashion. Even a 10 to 20% reduction in returns can save retailers millions while cutting thousands of tonnes of emissions each year.

A Win-Win for Consumers and Retailers

RealClo operates as a B2B subscription service, with flexible pricing based on company size and sales volume. Smaller brands can join affordably, while larger retailers pay proportionally more. Consumers enjoy a more personal and confident shopping experience; retailers save costs and improve sustainability performance. Over time, RealClo’s AI learns from aggregated data to enhance fit predictions and visualization accuracy.

RealClo marks a shift toward a more inclusive and sustainable digital fashion future: where shopping online feels personal, transparent and environmentally conscious.

References

Stöcker, B., Baier, D. & Brand, B. (2021). New insights in online fashion retail returns from a customers’ perspective and their dynamics. Journal of Business Economics, 91, 1149–1187. https://doi.org/10.1007/s11573-021-01032-1
Kong, D., Seock, Y., Marschner, S. & Park, H. (2023). Leveraging 4D Golf Apparel Wear Simulation in Online Shopping: A Promising Approach to Minimizing the Carbon Footprint. Sustainability. https://doi.org/10.3390/su151411444
Chrimes, C., Boardman, R., Vignali, G. & McCormick, H. (2022). Investigating how online fashion product page design affects the consumer’s clothing fit appraisal. Journal of Consumer Behaviour. https://doi.org/10.1002/cb.2100
Guirdham, O. (2023, 12 December). Global Fashion E-commerce Market to Exceed $1.2 Trillion by 2027, Driven by Segmentation and Tech Integration. EIN Presswire. https://www.einpresswire.com/article/673522654/global-fashion-e-commerce-market-to-exceed-1-2-trillion-by-2027-driven-by-segmentation-and-tech-integration
Thakker, A. M., et al. (2023). Sustainable development goals for textiles and fashion. National Center for Biotechnology Information. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10567856/

Contributors
747099AA – Audrya Kerenhappukh
613406AL – Aukje Land
667386IP – Ian Parabirsing
782803ZZ – Zetong Zhou Zhou

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It starts like this, someone texts their family group chat, slightly panicked:

“You won’t believe this. An old classmate just walked into our house. I think he’s homeless now. He’s sitting on the sofa and drinking coffee!”

They attach a photo of a scruffy man sitting comfortably in the living room, half-smiling, a mug in his hand. The curtains, the sofa, the lighting; everything looks exactly like their house. It seems real. Parents panic, call the police and within minutes, patrol cars are on their way.

But the man does not exist. He was created using a text-to-image generator as part of a new social media trend where people edit a stranger into photos of their own homes. With recently developed new AI tools, it is super easy to make a person appear realistic in almost any scene. You can adjust the pose, the light, the reflections and even the texture of their clothes.

For people who regularly work with these tools, there are small signs that give it away: hands that look slightly strange, soft textures that blur in the wrong places or lighting that doesn’t quite match the rest of the room. But parents who receive such a picture on WhatsApp do not see those details most of the time. They only see someone who shouldn’t be there and they react immediately.

The Dutch police yesterday warned about this exact prank after several emergency calls were made by parents who thought a homeless person had broken into their home. According to NOS, some even sent officers and helicopters before realising it was an AI-generated joke.

Although the trend may sound funny at first, it shows how easily generative AI blurs the line between imagination and reality. What begins as a joke can quickly create confusion and lead to unnecessary emergency responses. This also raises the question about responsibility of AI developers. How can they prevent such misuse? One possible solution would be to include automatic watermarks or origin tags in generated images. These would not be visible to the eye but could be detected by social media platforms or authorities. Another solution could be just clearer labelling on AI-edited content so that all users instantly know when an image has been altered.

Still, it is easy to understand why this trend became popular. The technology makes it effortless to create something that looks convincingly real. Tools such as DALL·E or Photoshop’s “Generative Fill” make that possible within seconds. I actually tried it myself, within five minutes I had a sheep standing in my hallway, and it looked suprisingly real.

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Quantum computing is often described as the technology that could change everything. Instead of working with zeros and ones, quantum computers use qubits that can exist in multiple states at once. This allows them to perform extremely complex calculations: from optimizing global logistics to speeding up drug discovery or running financial simulations.

In recent years, interest in quantum technology has grown rapidly. Universities, startups, and tech giants around the world are investing heavily in research and development. Companies like QuantWare and Qblox are building quantum chips and control systems, while IBM and Google are racing to achieve quantum advantage, the point where quantum computers outperform classical ones. Even investment funds such as Quantonation now focus entirely on quantum startups. It’s clear that a new industry is emerging and with it, creative business models that try to make quantum more accessible.

One company leading that charge is QCentroid, a Spanish startup founded in 2022. Instead of building its own hardware, QCentroid offers Quantum-as-a-Service (QaaS). It has build a cloud platform that allows businesses to develop and test quantum applications without investing in expensive equipment. Through intuitive no-code tools, even users without deep technical knowledge can run quantum algorithms. The platform also includes a marketplace where developers can share or sell ready-made solutions, essentially an “App Store” for quantum computing.

What I find most interesting is how this model changes the rules of the game. Much like cloud computing did for IT, QCentroid shifts the focus from ownership to access. Companies no longer need to buy hardware, they can simply rent quantum power when needed. This not only makes the technology more affordable but also more inclusive, opening the door for mid-sized firms and researchers to join the quantum revolution.

To me, QCentroid shows how IT can go beyond supporting innovation, it can actively disrupt and reshape markets and industries. By making access scalable and knowledge shareable, it represents exactly the kind of IT-enabled transformation that defines digital disruption today. The big question remains: will Quantum-as-a-Service grow fast enough to prove its value before quantum hardware becomes widely available? Either way, the future of quantum is closer than we think.