The fashion industry’s waste problem is often most visible in discarded garments piling up in landfills, but a significant portion of that waste occurs much earlier—on the factory floor, before a single item ever reaches a store. A new wave of AI-enabled quality control technologies is beginning to change that reality. Among the companies leading this shift is Smartex, a textile-technology startup using artificial intelligence and camera systems to spot defects in fabric during production, helping manufacturers reduce waste, improve efficiency, and cut environmental impact at scale. The company’s progress raises an important question: can smarter factories meaningfully shrink one of fashion’s most persistent waste streams?

Spotting Waste Where It Begins: AI-Driven Defect Detection

The fashion sector is notorious for its environmental footprint, with the Ellen MacArthur Foundation estimating that one truckload of textiles is landfilled or incinerated every second. While these end-of-life challenges receive most of the attention, large volumes of waste emerge upstream. Defective fabrics are often discarded before they even enter the cutting or stitching stages. Smartex aims to intervene at this early stage. The company’s system uses high-resolution cameras and machine-learning models to continuously analyse fabrics in real time as they run through production lines. By detecting flaws early, sometimes within seconds factories can halt and adjust production before entire rolls are compromised. Over the past three years, Smartex reports that its technology has prevented roughly 1 million kilograms of fabric from being wasted. The company estimates an efficiency gain of approximately 0.37% per kilogram of finished fabric. Though modest on paper, such improvements compound dramatically across global supply chains where thousands of tonnes of raw material move through factories each year. In 2024, for example, Inditex used more than 678,000 tonnes of raw materials. At that scale, even fractional improvements translate into reductions in waste, cost and emissions.

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The Challenge: A Fragmented, Manual and Global Supply Chain

One reason waste is so difficult to address is the complexity of fashion manufacturing. Cotton may be cultivated in India, dyed in China, knitted in Turkey, and stitched in Vietnam. Each step introduces opportunities for errors, delays and material losses yet visibility is limited and digital systems are often outdated. In an interview with CNBC, Smartex co-founder and CEO Gilberto Loureiro described the problem bluntly: despite being one of the world’s largest industries and one of its largest polluters, fashion manufacturing remains technologically underdeveloped. “There is a massive gap here,” he said, highlighting the industry’s slow digital transformation. This underinvestment in technology has real environmental consequences. The EU attributes nearly 20% of global water pollution to dyeing and finishing processes. When defective fabric is discarded, the energy, water and chemicals used to produce it are effectively wasted, compounding fashion’s overall ecological toll.

Investor Momentum and Technological Foundations

Smartex’s early results have drawn significant investor interest. In 2022, the company raised $24.7 million in a round led by Tony Fadell, best known as the inventor of the iPod through Lightspeed Venture Partners. H&M Group joined as a strategic investor, signalling industry appetite for scalable solutions that can reduce upstream waste. Altogether, Smartex has raised more than $40 million, positioning it among the better-funded climate-tech players in the fashion sector. Amazon’s AWS Compute for Climate Fellowship has also supported the company, providing cloud computing infrastructure and specialised tools for training and deploying Smartex’s machine-learning models. These models must be highly adaptable, learning to recognise irregularities across different materials, lighting conditions, and production environments. Loureiro spends much of his time visiting factories in Bangladesh, Vietnam and other major hubs to build trust and demonstrate value to manufacturers. Convincing factory owners requires proving a quick return on investment.

Toward a Digital Operating System for Fashion Factories

While Smartex’s current business focuses on defect detection, Loureiro envisions a broader transformation. Over time, the company aims to become the digital backbone of textile production: tracking the origins of materials, documenting production conditions and enabling real-time traceability across the supply chain.

“Most fashion brands cannot answer basic questions about where their products come from and what resources were used,” he said. Smartex sees itself evolving into an “operating system” for the industry, one that integrates information across factories much like Apple’s ecosystem links its devices.

Tony Fadell has echoed this analogy, noting that the value lies not in each individual component but in how they work together. For a sector under rising pressure to prove environmental and social claims, such transparency is increasingly becoming a competitive necessity.

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A Glimpse of Fashion’s AI-Powered Future

Smartex offers a preview of how artificial intelligence could reshape one of the world’s least digitised and most polluting industries. By addressing waste where it originates, improving production accuracy and enabling new levels of data visibility, AI-enabled systems could support a shift toward more efficient, traceable and sustainable manufacturing. Yet the company’s journey also underscores the challenges ahead. Fragmented supply chains, inconsistent standards, and tight margins make widespread adoption a slow process. But the scale of potential impact is significant: reducing waste upstream improves environmental outcomes, lowers costs, and lays the foundation for a more circular fashion economy. As brands and regulators push for greater accountability, innovations like Smartex may become essential tools in an industry under pressure to reinvent itself, one metre of fabric at a time.

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