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Max Fashion Brings AI Virtual Try-Ons To Gulf Online Shoppers

Landmark Group’s value fashion brand is using Google Cloud’s generative AI to tackle the returns problem that has dogged ecommerce since its beginning.

Published

5 months ago

June 13, 2026

Joanna Melki

max fashion brings ai virtual try-ons to gulf online shoppers

Buying clothes online has always involved a gamble. A garment that looks right on a model may hang differently on the person ordering it, and the result is a cycle of returns that costs retailers money and customers patience. Max Fashion, part of Dubai-based Landmark Group, is betting that generative AI can improve the experience.

The brand has launched what it describes as one of the region’s first virtual try-on experiences, built on Google Cloud’s Virtual Try-On API and generative AI vision models delivered through the Gemini Enterprise platform. Starting in the UAE, shoppers browsing Max’s digital platforms can see realistic previews of how garments drape, fit and move across different body types before committing to a purchase.

google cloud max fashion partnership

For many online shoppers, uncertainty is the single biggest barrier between scrolling and buying. “It helps address real purchase barriers, particularly around fit and confidence, while allowing us to create a richer and more engaging shopping journey,” explained Hani Weiss, chief executive officer of Max Fashion, who framed the rollout as part of the brand’s ambition to make fashion more accessible.

Bala Subramaniam, senior vice president and head of omnichannel at Max, seemed even more enthusiastic about the technology: “For the first time, a customer browsing on their phone has the same confidence as one standing in our fitting room”.

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Whether AI previews can genuinely match a fitting room remains to be proven at scale. The technology’s value will depend on how accurately it renders fabric and fit across the full range of bodies that shop at a value fashion brand, and on whether shoppers trust what they see enough to change their behavior.

For Google Cloud, the deployment is also a statement about where regional retail is heading. “AI-driven personalization is no longer a luxury, it is a core business imperative for forward-thinking retailers,” says Ziad Jammal, general manager for Google Cloud UAE, Levant and North Africa. If the returns data eventually backs that up, the rest of the region’s retailers will be watching closely.

Related Topics:Artificial Intelligence Google Google Cloud

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ESET Bets On Specialized Security AI At 0.5% The Training Cost

ESET says NeoCortex can estimate an attacker’s next move, with a kill switch and human oversight built in to keep it from going rogue.

Published

5 days ago

November 4, 2026

Dana Saadeh

eset bets on specialized security ai at 0.5% the training cost

A sign-in anomaly, a suspicious process launch and an unusual connection may look harmless alone. Together, they can describe an intrusion that is hard for security to teams to piece together.

To solve that problem, ESET announced ESET NeoCortex on November 4, 2026: a specialized foundational AI model for cybersecurity. It powers ESET LiveCortex, available within the ESET PROTECT Platform, which connects fragmented security signals and turns them into actionable incidents.

Filtering is the main selling point, with LiveCortex processing billions of raw telemetry events daily, and just 0.00111% of them contributing to incidents presented to customers. The aim is less alert fatigue and more analyst time spent on threats that matter.

NeoCortex is built on world-model architecture, learning how digital environments behave and how events relate. ESET says that lets it reconstruct missing incident context, recognize similar past cases and estimate an attacker’s likely next step – what the company calls “minus-one-day defense,” a chance to intervene before the next stage unfolds. ESET points to Black October, the week leading up to October 25, 2026, when critical security issues affecting PHP, Linux, GitLab and other key systems demanded rapid, prioritized responses, as the kind of moment where that context matters.

ESET’s internal research also suggests a specialized model like NeoCortex can be trained at up to 200 times lower cost than a large general reasoning model, while delivering more accurate, defense-focused results. The company credits optimized training, a focus on cybersecurity vocabulary, lower computing requirements and privately owned hardware.

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“AI is accelerating both cyberattacks and cyberdefenses. With ESET NeoCortex, we are applying our AI investment to develop specialized foundational cybersecurity intelligence that remains under our control and strengthens, rather than replaces, human expertise,” said Juraj Malcho, Chief Technology Officer at ESET.

As part of the design, NeoCortex forms part of a layered architecture combining specialized models, deterministic controls, correlation logic, validation, auditability and expert human oversight. Safeguards include restricted sandboxes, read-only tools, continuous monitoring, defined usage and token limits, a kill switch and human oversight of critical response decisions. The model is also developed and self-hosted on ESET’s own infrastructure, which the company says reduces exposure to shifts among third-party AI providers, including commercial and geopolitical ones.