Anastasia Braitsik stands as a towering figure in the digital marketing landscape, renowned for her ability to decode the complex interplay between search algorithms, data analytics, and human behavior. As a global leader in SEO and content marketing, she has spent years navigating the shifts from traditional keyword targeting to the sophisticated world of entity-based search and generative AI. Her philosophy is rooted in the belief that the most honest answer in marketing is often “it depends,” a mantra that reflects her deep commitment to context over rigid checklists. In this discussion, we explore her strategic approach to technical debt, the rising influence of generative engines, and why a one-year-old site can sometimes leave a decade-old domain in the dust.
The following conversation delves into the nuances of modern search strategy, moving beyond surface-level tips to uncover the deeper logic required to win in 2025 and beyond.
Schema markup helps search engines and generative models interpret content, but its impact on rankings is often indirect. How do you decide which structured data to prioritize for an e-commerce site versus a news publisher, and what metrics should be used to measure its success beyond rich result eligibility?
When deciding on a schema strategy, I look at the specific “real estate” we are trying to occupy within the search results. For an e-commerce client, my priority is almost always product snippets, pricing visibility, and review stars because these elements have a visceral impact on trust and click-through rates. For a news publisher, the focus shifts entirely toward meeting the requirements for Top Stories, Google Discover, and other news-specific carousels where visibility is highly time-sensitive. Success shouldn’t just be measured by a “valid” status in a search console; we look at the delta in click-through rates and how effectively Large Language Models (LLMs) are able to cite and interpret the site’s information. As confirmed by experts at Microsoft Bing, this markup acts as a roadmap for LLMs to synthesize your content accurately, so we track how often our data points appear in generative summaries as a key KPI.
Traditional search focuses on ranking documents while generative engines synthesize and generate responses. What specific workflow adjustments are necessary when shifting from document-level optimization to influencing generative systems, and how do you balance overlapping tactics like entity relationships and internal linking across both platforms?
The shift from document-level ranking to generative synthesis requires a move toward “atomic” content design, where information is structured so it can be easily extracted and reassembled by an AI. In traditional SEO, we might focus on a page’s total authority, but for Generative Engine Optimization (GEO), we must emphasize the clarity of entity relationships—how clearly a person, place, or concept is connected to another. We balance this by maintaining strong internal linking for bot accessibility while simultaneously ensuring our structured data is flawless to help LLMs “understand” the context of those links. While the mechanics differ—one retrieves a file and the other generates a response—the core pillars of content quality and discoverability remain the common thread. It is about being the most reliable source of truth that an engine can confidently use to build its answer.
Many technical checklists flag all 404 errors as urgent, yet they often occur naturally as content evolves. In what scenarios, such as site migrations or high-backlink profiles, do these errors become critical performance risks, and how should a team determine the opportunity cost of fixing them?
A 404 status code is not an inherent penalty, and treating 10 broken links on a million-page site as a crisis is a waste of developer resources. However, the context changes instantly if those 10 URLs are “power pages” that hold valuable external backlinks or are central nodes in your internal linking structure. During a site migration, if a significant percentage of indexed URLs suddenly return 404s, it signals a massive loss of equity and can tank the visibility of the entire domain. We determine the opportunity cost by asking if these pages are currently ranking for time-sensitive keywords or if users are repeatedly encountering them, leading to a poor experience. If the broken links are just old, unlinked blog posts from five years ago, I tell my team to ignore them and focus on tasks that actually move the needle on revenue.
Newer websites can sometimes outrank established domains despite lacking a long-term history. What strategic advantages allow a one-year-old site to surpass older competitors, and how can a new brand leverage social presence or underserved queries to accelerate its visibility in a highly competitive niche?
Domain age is not a direct ranking factor, and a one-year-old site has the advantage of being “born” in the current search climate without the baggage of legacy technical debt or outdated content. A new brand can leapfrog competitors by aggressively targeting underserved queries—those specific, niche questions that established giants have become too broad to answer effectively. By pairing high-quality, laser-focused content with a strong social media presence, a new site creates a “brand signal” that tells search engines people are actively looking for them. This creates a sense of topical authority that can outweigh the sheer longevity of an older, slower-moving domain. It’s about being more relevant and more agile than the incumbents who are resting on their historical laurels.
Professional SEO requires moving away from one-size-fits-all checklists toward context-heavy decision-making. When training a team, how do you teach them to identify which factors matter for a specific business model, and what steps should they take to diagnose whether a “standard” rule actually applies?
I teach my team that the most important skill in SEO isn’t memorizing a list, but asking the right diagnostic questions before touching a single line of code. We start by analyzing the specific backend, the competition in the niche, and the user intent behind the primary keywords to see if a “standard” rule even makes sense. For example, duplicate content is generally “bad,” but in some technical or legal industries, specific phrasing must be identical across pages, and we have to account for that. I encourage them to look at the “how many” and “how fast” of any issue—diagnosing if a problem is a site-wide systemic failure or just a minor outlier. True expertise comes from knowing when to break the rules because the specific business context demands a more nuanced approach than a generic checklist provides.
What is your forecast for GEO?
My forecast for Generative Engine Optimization is that it will become an inseparable layer of the standard SEO workflow, where “visibility” is no longer defined just by a blue link, but by the frequency and accuracy of a brand’s mention in AI-generated answers. We are moving toward a 2025 and 2026 landscape where the winners will be those who provide the most structured, entity-rich data that LLMs can digest without friction. While traditional search isn’t going away, the “synthesis” model of information retrieval will force us to prioritize authoritative, data-backed content over long-form fluff. Ultimately, GEO will reward websites that act as the definitive knowledge graph for their specific niche, making the “it depends” mindset even more critical as we optimize for both human readers and generative algorithms.
