Wove app scans clothing for PFAS and microplastics



TL;DR

Wove is a new mobile app that scans clothing for PFAS, microplastics, and hidden toxins, giving shoppers plain-language safety ratings and cleaner alternatives. Launched amid rising regulatory pressure and renewed public concern following Netflix’s The Plastic Detox, it aims to do for wardrobes what Yuka did for grocery aisles.

 

Most health-conscious consumers have already ditched plastic containers, switched to filtered water, and overhauled their skincare routines. Clothing, however, remains a stubborn blind spot, and a new app called Wove wants to change that.

Launched this week, Wove bills itself as the first mobile app that scans everyday garments for PFAS, microplastic shedding potential, and other hidden toxins. Users can upload a photo, screenshot, clothing tag, product description, or shopping URL, and the app returns a plain-language rating based on fibre composition, chemical concerns, and microplastic risk. If the score is poor, Wove recommends cleaner alternatives that match the shopper’s style, lifestyle, and budget.

The comparisons to Yuka, the popular food and cosmetics ingredient scanner with more than 80 million users worldwide, are inevitable. Like Yuka, Wove positions itself as fully independent, ad-free, and free of paid brand placements or sponsored rankings. The difference is the domain: instead of scanning barcodes on cereal boxes, Wove focuses on the synthetic materials draped over your body every day.

The timing is deliberate. Netflix’s documentary The Plastic Detox, which premiered in March 2026, has reignited public concern over synthetic materials, chemical exposure, and their links to fertility problems. That cultural moment sits alongside a growing body of regulation: France banned PFAS in textiles as of January 2026, California’s AB 1817 already prohibits intentionally added PFAS in clothing, and the EU is tightening its REACH restrictions on related substances this year.

The underlying data makes the case for scrutiny. Synthetic fibres have grown from roughly 45% of global fibre production in 1996 to around two-thirds today, with polyester alone accounting for more than half of all fibre output worldwide. Every wash cycle sheds microscopic plastic particles into waterways, yet a 2025 survey for the National Cotton Council found that only 42% of consumers who are aware of microplastic pollution actually connect it to their clothing.

PFAS, the so-called “forever chemicals” prized for water and stain resistance, present a parallel concern. Studies have linked PFAS exposure to endocrine disruption, reduced fertility, immune suppression, and several cancers. The chemicals are notoriously persistent, taking thousands of years to break down in the environment, and they have been detected at hazardous levels across thousands of sites in Europe alone.

Wove was founded by Emily Hemphill, a product leader based in Charlotte, North Carolina, whose own fertility and wellness journey led her to investigate what was in her wardrobe. “Clothing is often the last blind spot,” Hemphill has said, describing the gap between the attention consumers pay to food and skincare ingredients and the near-total lack of transparency around textile chemistry.

The app is currently available on the Apple App Store, with an Android waitlist now open. Whether Wove can replicate Yuka’s viral adoption trajectory remains to be seen, but it enters a market where consumer demand for transparency is clearly outpacing what fashion brands voluntarily disclose. In an industry where microplastic pollution is measured in millions of metric tonnes per year, giving shoppers a way to see what their clothes are actually made of feels less like a niche feature and more like overdue infrastructure.



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ZDNET’s key takeaways

  • Trusted quality data is the backbone of agentic AI.
  • Identifying high-impact workflows to assign to AI agents is key to scaling adoption.
  • Scaling agentic AI starts with rethinking how work gets done. 

Gartner forecasts that worldwide AI spending will total $2.5 trillion in 2026, a 44% year-over-year increase. Spending on AI platforms for data science and machine learning will reach $31 billion, and spending on AI data will reach $3 billion.

The global agentic AI market will reach $8.5 billion by the end of 2026 and nearly $40 billion by 2030, per Deloitte Digital. Organizations are rapidly accelerating their adoption of AI agents, with the current average utilization standing at 12 agents per organization, according to MuleSoft 2026 research. This rate is projected to increase by 67% over the next two years, reaching an average of 20 AI agents. 

Also: How to build better AI agents for your business – without creating trust issues

According to IDC, by 2026, 40% of all Global 2000 job roles will involve working with AI agents, redefining long-held traditional entry, mid, and senior level positions. But the journey will not be smooth. By 2027, companies that do not prioritize high-quality, AI-ready data will struggle to scale generative AI and agentic solutions, resulting in a 15% loss in productivity. While 2025 was the year of pilot experiments and small production deployments of agentic AI, 2026 is shaping up to be the year of scaling agentic AI. And to scale agentic AI, according to IDC’s forecast, companies will need trustworthy, accessible, and quality data. 

Scaling agentic AI adoption in business requires a strong data foundation, according to McKinsey research. Businesses can create high-impact workflows by using agents, but to do so, they must modernize their data architecture, improve data quality, and advance their operating models. 

McKinsey found that nearly two-thirds of enterprises worldwide have experimented with agents, but fewer than 10% have scaled them to deliver measurable value. The biggest obstacle to scaling agent adoption is poor data — eight in ten companies cite data limitations as a roadblock to scaling agentic AI. 

Also: AI agents are fast, loose, and out of control, MIT study finds

McKinsey identified the top data limitations as primary constraints that companies face when scaling AI, including: operating model and talent constraints, data limitations, ineffective change management, and tech platform limitations. 

Data is the backbone of agentic AI

Research shows that agentic AI needs a steady flow of high-quality, trusted data to accurately automate complex business workflows. Successful agentic AI also depends on a data architecture that can support autonomy — executing tasks without human intervention. 

Two agentic usage models are emerging: single-agent workflows (one agent using multiple tools) and multi-agent workflows (specialized agents collaborate). In each case, agents will rely on access to high-quality data. Data silos and fragmented data would lead to errors and poor agentic decision-making. 

Four steps for preparing your data 

McKinsey identified four coordinated steps that connect strategy, technology, and people in order to build strong foundational data capabilities. 

Also: Prolonged AI use can be hazardous to your health and work: 4 ways to stay safe

  1. Identify high-impact workflows to ‘agentify’. Focus on highly deterministic, repetitive tasks that deliver value as strong candidates for AI agents. 

  2. Modernize each layer of the data architecture for agents. The focus on modernization should support interoperability, easy access, and governance across systems. The vast majority of business applications do not share data across platforms. According to MuleSoft research, organizations are rapidly adopting autonomous systems. The average enterprise now manages 957 applications — rising to 1,057 for those furthest along in their agentic AI journey. Only 27% of these applications are currently connected, creating a significant challenge for IT leaders aiming to meet their near-term AI implementation goals. 

  3. Ensure that data quality is in place. Businesses must ensure that both structured and unstructured data, as well as agent-generated data, meet consistent standards for accuracy, lineage, and governance. Access to trusted data is a key obstacle. IT teams now spend an average of 36% of their time designing, building, and testing new custom integrations between systems and data. Custom work will not help scale AI adoption. The most significant obstacle to successful AI or AI agent deployment is data quality, cited as the top concern by 25% of organizations. Furthermore, almost all organizations (96%) struggle to use data from across the business for AI initiatives.  

  4. Build an operating and governance model for agentic AI. This is about rethinking how work gets done. Human roles will shift from execution to supervision and orchestration of agent-led workflows. In a hybrid work environment, governance will dictate how agents can operate autonomously in a trustworthy, transparent, and scaled manner. 

The work assigned to AI agents 

McKinsey highlighted the importance of identifying a few critical workflows that would be candidates for AI agents to own. To begin, an end-to-end workflow mapping would help identify opportunities for agentic use. McKinsey found that AI adoption is led by customer service, marketing, knowledge management, and IT. It is important to identify clear metrics that validate impact. Teams should identify the data that can be reused across tasks and workflows.

Also: These companies are actually upskilling their workers for AI – here’s how they do it

McKinsey concludes that having access to high-quality data is a strategic differentiator in the agentic AI era. Because agents will generate enormous amounts of data, data quality, lineage, and standardization will be even more important in the agentic enterprise. And as agentic systems scale, governance becomes the primary level for control. The data foundation will be the competitive advantage in the agentic era. 





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