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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1. From a strategic perspective, how crucial is the depth and recency of data in the effectiveness of AI-powered insights for your organization's growth initiatives?

No organization should ignore the potential of AI to improve marketing effectiveness and productivity. That being said, marketing leaders shouldn't  expect these tools to deliver the results if they’re being starved for data – that’s when you run into the unfortunate syndrome of chatbots “making stuff up” in an attempt to answer a question. The data being fed to the AI tool  needs to be as complete and recent as possible, so that the AI tool stack is well informed and knowledgeable about your company and your market.

That’s particularly critical as we move from chatbot interactions to deploying AI Agents that we expect to do work on our behalf, more autonomously over time. We need them to execute tasks based on hard data and well-defined plans.

Think about data being like fuel for an aircraft. Skimp on fuel, and you won’t get high performance. You may not make it to your destination.

2.In what ways do you believe specialized AI tools can provide a competitive advantage in areas like SEO, sales, and market trend analysis?

Specialization in the software and the data is how we make AI more productive and impactful for marketing and sales. In addition to providing AI tools with high quality and up to date data, we want to focus it on the problem we aim to solve.

Similarweb’s mission is to provide marketers with more and better data, but the beauty of adding AI agents into the mix is that we don’t have to worry as much about overwhelming the users. Similarweb’s AI agents can cover a lot of ground very quickly, meaning, they can accomplish in minutes what otherwise would be time consuming research tasks.

With a conventional user interface for a data system, we work on developing individual screens and charts and dashboards to help users understand and navigate through the data. But given the limitations of our poor human brains, as consumers of that data it still takes time to look at each screen, understand what it means, and maybe download data into spreadsheets for additional analysis.

Instead of looking at one screen or one spreadsheet export at a time, they can look at all the data and make sense of it for us. We get the answers we’re looking for, all together, not in bits and pieces.

That doesn’t mean you let them do all the thinking for you. You and your colleagues still have to review the analysis the agent has produced and determine whether it is sound and makes sense for the organization. You might want to do some spot checking of the underlying data, just as you would for a new employee whose work you don’t entirely trust. However, the odds that the data will be correct and the output will be useful are a lot higher if you work with an AI agent that is specifically trained on marketing and the data that is important to marketers.

3.The "AI Meeting Prep Agent" is cited for reducing research time. How critical is the efficiency gained from such tools in optimizing the productivity of sales and business development teams within your enterprise?

The AI Meeting Prep Agent is a good example of focusing the technology on a common business task or challenge. A good salesperson will go into a meeting prepared to achieve the best outcome, and it’s something they do many times every week, or even every day. The AI agent functions as a capable assistant who finds out your goals for the meeting and produces a thorough briefing on the people you will be meeting with and the opportunities and challenges of their business, based on news reports and company data as well as digital signals.

The reception from Similarweb Sales Intelligence customers has been very enthusiastic, with customers sharing feedback about saving hours of work and going into meetings better prepared. We have a similar story with our AI Content Strategist, which crunches data on SEO and competing companies, identifies content gaps, and makes specific recommendations.

In addition to the agents announced at the end of May, we’re working on a lead generation agent, a competitive intelligence agent, a stock research agent, and an ad creative agent — while scanning the horizon for other opportunities to build or buy additional products. We also have several projects under way to create agents for our own internal use.

4.How can rapid AI-driven insights into emerging market shifts directly inform and accelerate your strategic decision-making processes?

Everyone who works in digital marketing knows how fast things change. Even before the current panic over changes in Google search (search clicks decreasing as summary AI Overviews appear in the results more often), digital marketers were continually forced to adapt to changes in the algorithm. The same concept applies for marketing on Facebook, LinkedIn, or X. All of this has to be considered before marketers begin to evaluate changes in the competitive landscape. For example, look at how fast Temu went from nothing to a major ecommerce player – becoming the #2 most visited ecommerce website in the US by August 2024 – and how fast it retreated from the US market in the face of new tariff policies in the past few months, throttling back paid advertising so that it dropped to #11 in May.

Change is constant in marketing, and often it feels like a full-time job to stay on top of. Even with access to good data, marketers could use help sorting through it. This is where AI agents can assist. They take us beyond providing dashboards of data to providing analysis and making recommendations.

5.Looking ahead, what specific opportunities do you foresee in adopting and scaling AI Agent technologies across diverse departments within a large enterprise, and how do you plan to address them?

I love that question because the answer is: we’re just getting started imagining all the possibilities. The whole MarTech industry is going to shift to competing over who can deliver the best agents. And by best, I don’t mean the ones with the flashiest demos, I mean the ones that deliver practical results.

Our customers are just getting started sifting through all the AI-related pitches that are coming their way to determine what’s real and what’s smoke and mirrors. As they get more comfortable with this agent-driven generation of AI technology, they will be better equipped to ask for what will be useful to their organizations.

Don’t get me wrong, we’re already seeing strong demand and a pipeline of desirable AI agents that will keep us busy for a long time to come. I think the “scaling” limit is less about scaling technology than it is about learning to work with it productively. Enterprises will need to scale their leadership in AI adoption, educating employees on embracing their new AI team members and putting them to work productively.

At the same time, skepticism is entirely warranted. Every MarTech product is going to claim to be an AI product as part of the same old tech hype cycle. Tech buyers will need to sort out what’s real. It’s my job to ensure Similarweb comes down on the right side of that.

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1. How is your marketing team managing manual processes in terms of influencer relationships, and how are you addressing scalability challenges?

A lot of brands still rely on spreadsheets, manual outreach, and disconnected tools to manage influencer programs which makes it nearly impossible to scale efficiently. Once a brand grows from 10 to 50+ influencer partnerships, the wheels start to fall off. Teams get bogged down in manual follow-ups, managing approvals, handling gifting logistics, and compiling performance reports. It ends up consuming their entire bandwidth. That’s where automation changes everything. Influencer marketing platforms, like Endlss, that combine outreach, gifting, commission tracking, and communication in one place have become essential to keeping programs scalable. With the right tools, marketing teams can manage 3x the creator volume without needing to grow their headcount, freeing up time for the work that actually drives results.

2. How is your organization evolving its influencer marketing strategy to shift from brand awareness to measurable revenue generation?

Influencer marketing used to be all about reach and impressions, but the most forward-thinking brands today are treating it like a true performance channel. Instead of chasing vanity metrics, they’re focused on driving measurable, attributable growth. To meet that demand, more teams are adopting attribution tools that link creator content to conversions—whether through custom landing pages, affiliate links, or dynamic tracking infrastructure. On our end, we’ve built SmartLinks into the core workflow, so each creator’s impact is measured in real-time, and with partners of ours like Creator Commerce, together we provide co-branded shopping sites to elevate the consumer experience with a trusted shopping experience that increases conversions. Weekly performance reports make it easy to see which partnerships are generating returns and which need to be re-evaluated. That kind of visibility helps transform influencer marketing from a brand play into a predictable revenue stream.

3. How are you approaching influencer selection and outreach to ensure alignment with your brand values and audience segments at scale?

Alignment is everything in influencer marketing and not just in terms of values. The right creator should reflect the brand’s tone, speak to the right audience segment, and have a track record of driving action. Brands are getting more precise with how they vet creators, looking at engagement quality, audience breakdowns, content style, and past performance before making a move. With AI-powered messaging, every brand can personalize outreach in their own tone of voice—tailored to each creator’s audience, style, and past content.

But finding the right fit at scale is a different challenge. That’s where AI and smart filters are transforming outreach. With AI-powered messaging, every brand can personalize outreach in their own tone of voice—tailored to each creator’s audience, style, and past content. Combined with branded application forms and full creator analytics, brands are scaling high-quality outreach without losing that human touch. Inviting existing customers to apply is low hanging fruit when you want to scale effectively, and authentically—people who already know and love the brand often make the best partners.

4. What limitations have you encountered with traditional tracking methods (e.g., promo codes, UTM links), and how are you planning to evolve your attribution strategy?

Traditional tracking methods come with real friction. Promo codes can get leaked or shared in unintended ways, making attribution muddy. UTM links often break in-app or get stripped entirely, especially on mobile. This creates a gap between creator activity and the sales data that marketers rely on to optimize spend. To move past these limitations, we’re focusing on more robust attribution tools that work reliably across platforms and devices. SmartLinks, for example, generates unique tracking for each creator and integrates directly into conversion and payout workflows. Clean attribution is foundational to scaling today’s influencer programs responsibly. Whether it's to manage budgets or reward high performers, teams need to trust the data.

5. How are you evaluating new MarTech platforms to determine their potential impact on operational agility and cross-functional collaboration?

When evaluating MarTech tools today, agility is at the top of the list. Marketing teams need tools that are fast to implement, intuitive to use, and flexible enough to support cross-functional workflows. If a platform takes weeks to implement or requires engineering support to operate, it’s already a blocker. The best tools today integrate seamlessly with existing systems, whether that’s ecommerce platforms like Shopify, payment processors like Stripe, or internal communication tools. Endlss replaces four different tools in one, so brand, finance, and CX teams can all work from a single system. At the end of the day, the best platforms don’t just do more; they reduce friction across every team.

6. What competitive advantages do you see in adopting lean, AI-powered influencer marketing platforms compared to legacy tools with heavier infrastructure and higher costs?

Legacy influencer marketing platforms were often built with large enterprises in mind. They’re powerful, but also complex, expensive, and heavy to manage. For fast-moving teams, that’s become a real disadvantage—especially when speed and efficiency are critical. Lean, AI-powered platforms are flipping the script. By automating outreach, tracking, and gifting workflows, brands can move from idea to execution in 20 minutes, not weeks. And because these tools are often modular and self-serve, they’re far more cost-effective. What we’ve seen is most brands using Endlss are cutting their software spend by 50% or more while getting campaigns live that day, not weeks. That kind of agility has become a major competitive edge, especially for brands trying to maximize output with lean teams.

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 1) What were the key architectural decisions involved in building the AI-powered content personalization platform, and how did you address performance, latency, and scalability concerns?

As Storm Reply, as official AWS Premier Consulting Partner, our first architectural decision was to anchor the entire platform on Amazon Web Services (AWS). This strategic choice allowed us to take advantage of AWS’s robust AI/ML offerings and global infrastructure from day one. At the heart of the platform is Amazon Bedrock, which provides seamless access to multiple large language models (LLMs) from top providers like Anthropic and Meta. This not only gave us flexibility in model selection, but also ensured enterprise-grade reliability, availability, and speed.

To address performance, latency, and scalability:

• We utilized Amazon CloudFront for global edge caching, which significantly reduced latency,
• We benefited from Bedrock’s fully managed backend, which automatically scales based on workload without manual intervention,
• And we relied on AWS’s Tier 1 data centers and 99.99% SLA-backed availability to ensure high reliability across regions.

By designing a cloud-native architecture using AWS-native services, we were able to deliver a scalable, low-latency, and highly resilient platform with minimal operational overhead - aligned with both our technical vision and AWS best practices.

2) Can you walk us through how the solution integrates NLP and ML for content extraction and contextual adaptation across industries and formats?

The platform we built for Storybent leverages machine learning services provided by AWS through Amazon Bedrock, where access to multiple large language models - such as those from Anthropic, Meta, and others - is already built in. These models are wrapped in APIs that make it easy to plug into our workflow.

We use this setup to compare and fine-tune outputs across different LLMs, depending on the industry, content type, or language style required. By carefully crafting and adjusting prompts, we can generate highly specific, context-aware content that fits a variety of formats - from marketing copy to social media to technical descriptions.

This allows us to support a full end-to-end content pipeline: from the initial idea, through language understanding and generation, to producing tailored outputs optimized for both audience and channel.

3) What were the major implementation challenges faced when taking this AI-powered system from concept to production, and how were they overcome?

One of the key implementation challenges - common across many AI projects - was putting the right structure in place to trust the output of the system at scale. From an engineering perspective, the core components were in place, but the challenge was ensuring the generated content met the required standards across use cases.

To solve this, we implemented a human-in-the-loop workflow, where outputs were reviewed, approved, and continuously improved through expert feedback. This helped us validate results early on, fine-tune prompts, and build guardrails that ensured consistency and relevance across different industries and formats.

Over time, this approach evolved into a repeatable and scalable process. The models improved through iterative prompt design, and we established a feedback loop that allowed the system to gradually operate with more autonomy - without compromising quality or control.

4) What DevOps and MLOps frameworks have been integrated to ensure delivery, monitoring, and model updates in a production environment?

We chose Amazon Web Services (AWS) because of its strong support for both DevOps and MLOps at scale. From an MLOps perspective, the solution is built around Amazon Bedrock, which offers fully managed access to a variety of foundation models, as well as simplified deployment, monitoring, and billing transparency. This removes much of the operational overhead typically involved in managing generative AI workloads.

On the DevOps side, the platform is deployed using Amazon CloudFormation, enabling infrastructure as code and repeatable, automated deployments. We’ve integrated AWS Config, CloudWatch, and CloudTrail to support system configuration, performance monitoring, and auditing. These tools together power a CI/CD pipeline with DevSecOps practices, ensuring the platform remains secure, scalable, and easy to maintain.

We continue to prioritize native AWS services wherever fiscally feasible, in order to maintain tight integration, cost visibility, and long-term flexibility.

5) How do you foresee the role of GenAI evolving in enterprise content strategies, particularly in terms of personalization, real-time adaptation, and cross-channel orchestration?

GenAI is already becoming a foundational tool in enterprise content strategies, especially for personalization at scale and rapid content generation. But its real potential lies in how it integrates into automated workflows - where the goal is to go from a simple idea or brief to a complete set of outputs across multiple formats and channels.

Looking ahead, GenAI will play a central role in enabling real-time content adaptation, adjusting tone, format, and message dynamically based on audience, context, and platform. When combined with agents and orchestration tools, it will support cross-channel publishing - automatically generating tailored content for social media, email, print, and even video or audio.

In this context, GenAI isn’t just a content creation tool - it becomes part of a broader system that reduces time to market, lowers operational costs, and continuously optimizes content performance across touchpoints.

6) What innovations are you planning to add next to the platform—such as real-time audience segmentation, sentiment analysis, or multilingual support?

All of those capabilities - real-time audience segmentation, sentiment analysis, and multilingual support - are part of the roadmap. We’re working closely with Storybent to prioritize these features based on their business goals and rollout strategy.

That said, the area we’re most focused on next is building a system-level optimization strategy. Beyond adding features, the goal is to create a platform that’s constantly learning and improving - streamlining content delivery, reducing time to output, lowering overhead, and enhancing performance.

In a landscape where more companies are looking to insource AI capabilities, the ability to deliver continuous, automated optimization becomes a real differentiator. That’s where we see the greatest long-term value, and where we’re directing most of our innovation efforts.

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1. What role do you see FAST channels playing in your overall content distribution and monetization strategy?

a. People spend more time watching others play games than actually playing those games.
b. Medal users consumed more than 3B hours of game clip content on Medal’s social clipping platform. Billions of hours were consumed off platform when those clips were shared to Discord, video platforms, and other social networks. There’s clearly interest and demand for short form video game content for which Medal has cornered the market.
c. Syndicating this content to gaming FAST channels provides another outlet for gamers to connect to relevant gaming clips.

2. How is your company capitalizing on the convergence of traditional publishing, digital video, and OTT to create integrated media ecosystems?

a. Right now, our goal is to expand the number of people capturing and sharing their in-game memories with their friends on and off Medal and providing the best possible platform to both capture those moments and share them with those you actually care about. We’re not building a platform to make you famous. We’re building a platform to save memories that happen in the 3rd-space which is gaming for so many of us.

3. What frameworks or criteria do you use to evaluate the success and ROI of creator partnerships within your content strategy?

a. From a marketing perspective, we’ve worked with a lot of creators in the past and it’s a viable channel though I’m happy to share some hot takes if you’d like. From a product perspective, a creator program isn’t a near-term focus namely because Medal is more of a Snapchat-like experience. You share your in-game memories with those you play with and those that you have a connection with. Some people want to make these very public, and we’re happy to facilitate that however the majority of our users share within their circle of friends off platform - in places like group chats, discord etc. and they are doing it on average 7x a day. It's in those more intimate communities that GenZ has the most influence amongst their peers and Medal is now giving advertising access to these typically hard to reach moments of engagement.

4. In the age of fragmented media consumption, how are you aligning content formats and experiences across mobile, social, and digital to meet changing audience behaviors?

a. Niche social platforms are the future. The one-size-fits-all solution that most other social media provides–the competitive algorithm, the horrendous signal:noise ratio, the constant race to the bottom–sucks. Full stop. Medal is a platform for you and the people you spend time playing games with – a lot of time. Medal users spend 23 hours/week playing games. That’s a lot of time hanging out with friends in digital worlds. That’s a lot of time having core social experiences online, with no way to document, capture, and share like we have with our iphones in the real world. We’ve built that iphone for digital memories. For ephemeral experiences, the reception seems to be a resounding “hell yes” from our users. So it’s less about adjusting for the changing landscape and user behavior and more so building the landscape and creating the behavior change.

5. How do you balance short-term monetization goals with long-term audience and brand equity when scaling new digital products or channels?

a. Because Medal’s core brand promise is to connect gamers through their shared in-game experiences, we prioritize product quality above everything else. If our recorder fails and you miss that moment your friend said something silly or you and your friends finally cleared that impossible dungeon or beat that crazy hard boss, we’ve failed as a product. As a corollary, we prioritize user experience over monetization especially when it comes to ads. Our ads products are a combination of standard IAB formats of which we have only a few and Bounties, a deeply integrated, interactive ads product consisting of clip contests, rewarded in-game actions, and various clip creation and sharing-based activations. Ad saturation is something we want to avoid. It’s bad for users and it’s bad for advertisers. We also never show ads while the player is in-game, has Medal covered, minimized, or is AFK. Ad fraud is unfortunately rampant and we want no part in it. Building strong brand equity both in the gaming industry among players and in the media industry among advertisers is a tricky needle to thread but I think we’ve done a good job thus far.

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