AI & the New Era of Online Brand Protection – Matteo Amerio

1. In what ways do you define success in online brand protection today, and how does that differ from older models?

Success in brand protection is no longer about playing whack-a-mole with takedowns. The old model was a volume game—counting how many listings you could manually remove. It was reactive and inefficient.

Today, we define success as achieving mastery over a brand’s online channels. This is a fundamental shift from a manual-hour-based approach to a strategic, data-driven one.

Success is a metric that is unique to each brand. For one, it might be reclaiming lost revenue. For another, it's about preserving brand equity or enforcing distribution policies. Our approach is to provide the data and tools to achieve that specific goal. If the goal is anti-counterfeiting to clean up online marketplaces, we will then measure success by how "clean" a brand's channels are, how cooperative platforms are, and the overall visibility of both authentic and counterfeit content. It’s about moving from simply chasing infringers to strategically controlling your online presence.

2. Can you explain how the Cleanliness Score™ is calculated and how brands can use it to assess their online health?

Think of the Cleanliness Score™ as a daily credit score for your brand's online health. It's a simple, powerful KPI that transforms an abstract problem into a measurable one. 

The calculation is the result of six years of focused R&D.

For brands, this score provides immediate clarity. They can see if their channels are 99% clean or 50% clean, track progress over time, and use this objective data to hold marketplaces accountable and focus enforcement where it's needed most.

3. How does the Deep Semantic Detection capability improve the detection of disguised or non-textual infringements?

Traditional search technology is like looking for a needle in a haystack by only searching for the word "needle." Our Deep Semantic Detection is like a bloodhound—it follows the scent of an infringement, even when the sellers are trying to cover their tracks.

It works by mimicking the complex path a determined buyer uses to find fakes. They don't just search "counterfeit Brand X watch" on a marketplace. They start on Google, find a discussion on Reddit, follow a link to a seller’s page, and then browse related items on a platform.

Our technology automates this "graph traversal" process. This approach excels for two key reasons:

1. It uncovers hidden networks of infringers who intentionally avoid using trademarked terms.
2. By following these intelligent pathways, itidentifies infringing content much faster and more accurately than traditional searches, cutting through irrelevant noise.

So while they might use vague phrases like "clover-style jewelry" instead of "Van Cleef & Arpels Alhambra," our system connects the dots and finds them anyway.

4. Can you walk us through how risk clustering and SKU detection improve threat prioritization and resolution?

When you're facing thousands of potential threats, you can't treat them all equally. Our strategy for intelligent prioritization relies on two core pillars: a sophisticated scoring system for ranking threats and granular data for precise, automated actions.

1. Risk Clustering: We move beyond a simple "high risk" flag with a more advanced, two-part scoring system. This ensures our clients' resources are focused on the threats that deliver the fastest, most significant impact.
2. SKU Detection: This provides the critical layer of data granularity needed for modern enforcement. This capability is especially powerful for managing grey market distribution and executing highly targeted enforcement strategies.

5. How customizable is the Corsearch Zeal 2.0 platform for brands with different risk profiles or industry-specific needs?

Corsearch Zeal 2.0 wasn't built with customization as an add-on; it's foundational to its architecture. The core logic engine is tailored to each brand's unique risk profile from day one.

This customization is both deep and practical. The Risk Score is calibrated using a "brand bible" we develop with each client, defining what constitutes an infringement for their specific products. The Enforceability Score is tuned based on the brand's exact enforcement rules and the known policies of the platforms they need to police. This means the sorting and prioritization of threats isn't based on a generic, one-size-fits-all algorithm. It’s a bespoke enforcement engine configured for a brand’s unique needs, whether they're in luxury goods, pharmaceuticals, or fast-moving consumer goods.

This deep adaptability extends beyond the core logic and into the entire workflow. Brands can configure everything from product categories and custom data labels to reporting dashboards. The platform adapts to the client's team structure and objectives, not the other way around. We provide a powerful, configurable engine; our clients build their ideal command center on top of it.

6. How does Corsearhc Zeal 2.0 adapt to evolving threats, such as generative AI content misuse or new marketplace behaviors?

Our defense against emerging threats is a proactive, data-driven feedback loop, not a static rulebook.

For new marketplace behaviors—like infringers using new visual tricks to hide logos—we constantly monitor platform data. Our Cleanliness Scores and platform cooperativeness metrics act as an early warning system. Because our AI models are designed for rapid retraining, we can quickly adapt our detection capabilities to recognize and neutralize new tactics at scale.

Regarding Generative AI, we see it as another vector of attack, but not an unbeatable one. AI-generated fakes are often trained on flawed or "dirty" data, as counterfeiters lack access to official brand assets. This process inevitably creates subtle but detectable errors—mistakes in packaging details, incorrect logo placement, or flawed product renderings.

Essentially, we fight AI with more sophisticated, specialized AI. Our systems are trained to spot these tell-tale imperfections. By maintaining this agile, data-centric approach, we ensure we are always prepared to analyze and counter new threats the moment they emerge.

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