MarTech Interview with Kartal Goksel, Chief Technology Officer at Seedtag

Kartal, your career spans leadership roles. Could you share a bit about your professional background and how your experiences have shaped your perspective as you drive innovation at Seedtag?
My background is in Computer Science. Twenty-five years ago, I applied for a software engineer position at CNET, one of the hottest, well-known internet companies back then. That software engineer position within CNET’s advertising technology team turned into a lengthy career in AdTech. From the start, I fell in love with the problem space and computational challenge AdTech provided. Over the years, I have seen the industry evolve from ads hardcoded into web pages to the real-time programmatic advertising we see today. As the industry matured and the technology landscape evolved, the scale grew larger and larger, and the problems to conquer constantly shifted. This constant evolution kept my brain buzzing and my love for AdTech alive. I have seen the early evolution of programmatic advertising at Pubmatic and had a great eight-year run as the CTO of Kargo.

Over the years, I’ve learned that we work for the marketers and partner with publishers and agencies. Yet at its heart, advertising is the art — and sometimes the game — of influencing human perception. That’s why I strive to always innovate for the end user.

At Seedtag, how are you redefining contextual advertising in an era where data privacy is more important than ever?
An AI made for pure contextual advertising doesn’t care about who’s behind the screen and their personal data. It is, in my opinion, the only privacy-first solution. Our technology focuses on predicting interest and intent from the content the user is currently reading or viewing. For example, we can differentiate a running enthusiast from a runner in the market for a new running shoe just by predicting intentionality based on the article the user is reading in real-time and aligning that with the right creative and messaging.

Neuroscience is not something that you often hear in conversations in advertising tech. How does this play a role in your AI models?
Leveraging neuroscience principles, our neuro-contextual AI, Liz, operates like the human brain—recognizing patterns, interpreting context, and responding dynamically to user interests, emotions, and intentions. The result is a more cohesive and intelligent system, capable of understanding how people think and delivering the outcomes modern marketers need.

Seedtag recently launched its AI Intention Models. What exactly are they, and why are they a game-changer?
Our Intention Models offer a context-driven and effective advertising approach that distinguishes between casual browsing and transactional readiness, identifying consumers who are ready to take action in real-time, without relying on personal data. Powered by contextual modeling based on relevant search terms, queries, and popular URLs, Liz identifies best-performing content for targeting action-ready audiences. By uncovering intentional cues in content, Liz groups articles based on their intention level, giving each article a unique intention score.

How do these models impact campaign performance compared to traditional targeting methods?
By optimizing bids for placements with higher intention scores, Seedtag ensures clients allocate their budgets more effectively, reaching the most engaged audiences. For example, Nissan partnered with Seedtag in Spain to boost visibility and consideration of the new Nissan Qashqai among potential buyers in the C-SUV segment, focusing on reaching users in the active evaluation phase by generating interest and high-quality leads. The campaign delivered outstanding results, with an average Cost per Quality Visit (CPQV) 68% below the target and a Cost per Lead (CPL) 35% below the goal. Additionally, qualified visits tripled compared to Nissan’s original KPI.

Where do you see the biggest opportunities for brands using contextual advertising today?
Traditional targeting built on third-party data, cookies, identity graphs, and personal identifiers is increasingly disconnected from the reality we live in Consumers’ demand for privacy, combined with regulatory pressure and platform restrictions, has made these strategies outdated. Contextual advertising is the only durable, privacy-first path forward.
Within that, contextual CTV and OLV represent a massive opportunity. Today, we can build far more powerful models using multimodal embeddings that analyze text, image, and video together. These models unlock richer signals about mood, tone, and intent than keyword matching ever could. Paired with the right creative, this allows brands to reach consumers at the ideal frame of mind — at the emotional inflection point where messaging resonates most.
A personal passion of mine is moving the industry from brand safety to brand responsibility. Contextual has too often been used as a blunt tool, blocking valuable journalism around “sensitive” but important topics. With AI, we can finally distinguish between harmful and informative content, enabling brands to align with trusted publishers without sacrificing reach. This not only protects brand image — it builds consumer trust by supporting credible, professional information ecosystems.

There’s often skepticism about AI in advertising. How do you ensure transparency and trust in your approach?
Liz is ethical by design, as it doesn’t know who is behind the screen and therefore performs unbiased, privacy-first targeting that respects user anonymity. Liz has been built and trained from the ground up in-house, ensuring Seedtag has complete control over how it’s aligned to fight against biases. Also, Liz has an internal review process where it’s checked against biases in its output. Following Seedtag’s internal ethical guidelines, Seedtag prioritizes data privacy, transparency, and responsible AI use to ensure Liz is always used ethically.

What’s next for Seedtag and the evolution of contextual intelligence?
What’s next for Seedtag is the evolution from contextual intelligence to neuro-contextual intelligence — and the activation of AI agents powered by these models. Our obsession is building models that go beyond relevance to truly capture the interplay of interest, intent, and emotion derived from the context of content.
The future is about aligning the real-time state of the consumer with the right brand and message at the right moment. By combining advanced multimodal analysis with neuro-contextual models, we’re creating an entirely new level of precision — where advertising doesn’t just reach people, but connects with them.

What advice would you give to brands and marketers looking to future-proof their advertising strategies in an AI-driven world?
I’d bring it back to what I’ve highlighted already: the real future-proof strategy lies in multimodal contextual advertising — spanning text, image, and video — combined with contextual DCO at scale. These approaches are becoming more powerful every day, and in many cases already outperform identity-based or data-driven activations.

We’re entering an era where advertising will be judged not only by effectiveness, but by how it respects consumers and contributes to healthier information ecosystems. Contextual is the bridge — delivering performance while honoring privacy and supporting credible publishers.

My advice is clear: lean in now. Brands that dedicate a larger share of their marketing budgets to contextual will not only see stronger performance, but also build lasting trust with consumers by innovating in a way that respects their privacy.

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