How SEMAI Builds Topic Clusters from a URL

SEMAI.AI builds topic clusters by analyzing a URL to extract core entities, mapping them to validated user query clusters based on funnel intent, and monitoring AI retrieval signals to identify which questions the content answers and where gaps exist. This process creates a content strategy map aligned with how users and AI systems seek information, moving beyond traditional keyword-based approaches.

URL Content and Entity Analysis

The initial step is a deep content analysis of the provided URL to extract its primary entities and establish a performance baseline in answer engines. The platform deconstructs the content to identify its core subjects and monitors real-world data to see how AI systems currently interpret and use the page’s information.

• Entity Extraction: Identifies the primary and secondary people, products, and concepts discussed and maps their relationships.
• AI Signal Monitoring: Analyzes server-log data and patterns in Google AI Overviews (GAIO) to understand which queries currently trigger the URL as a source.
• Performance Baseline: Establishes how the content currently performs in answer engines, providing a starting point for identifying improvement opportunities.

“Effective topic clustering begins with understanding not just what a page is about, but how AI systems currently use it to answer user questions.”

Mapping Topics to User Funnel Intent

The platform maps extracted topics to user intent by analyzing the types of questions associated with each core entity and assigning them to a specific buyer’s journey stage. This ensures topic clusters are strategically aligned with user goals, a core principle of Answer Engine Optimization (AEO) .

• Middle of Funnel (MOFU): Questions focused on comparison, evaluation, or implementation, such as “How to compare X and Y,” are flagged as consideration-stage intent.
• Bottom of Funnel (BOFU): Queries about pricing, purchase, or specific use cases, like “What is the price of Z,” are mapped to decision-stage intent.

Validating Clusters with AI Retrieval Signals

AI retrieval signals serve as direct evidence to validate topic clusters by confirming which specific user queries your content successfully answers within AI Overviews and large language models. These signals are fundamental because they provide empirical proof of the connection between your content and the user questions it resolves.

“AI retrieval signals provide empirical validation, shifting topic clustering from a theoretical exercise to a data-driven reflection of actual content performance.”

Query Clusters vs. Traditional Keyword Groups

Query clusters differ from traditional keyword groups by focusing on semantic intent rather than syntactic similarity, grouping the conversational questions a user asks throughout their decision-making process. This approach prepares content to answer a series of related follow-up questions, reflecting a more realistic user journey.

• Traditional Keyword Group: Groups syntactically similar terms like “blue running shoe” and “blue sneaker for running.”
• Intent-Based Query Cluster: Groups semantically related questions that reflect a user’s journey, such as “What are the benefits of X?”, “How does X compare to Y?”, and “Is X secure for enterprise use?”.

Identifying Content Gaps Within a Cluster

Content gaps are identified by comparing the complete set of user questions within an intent-based query cluster against the questions your existing content demonstrably answers. The platform cross-references this analysis with competitor coverage and AI retrieval data to pinpoint specific opportunities for content creation. A critical gap is identified when a URL has high traditional SEO visibility but low citation frequency in AI Overviews, indicating a need for more direct, answer-first content.

Adapting to Future Answer Engine Trends

The clustering process is inherently adaptable to future trends because it relies on real-time AI retrieval signals and user intent analysis, not static keyword lists. As AI models evolve and user queries become more conversational and complex, the system’s ability to map and track entire question journeys ensures the topic clusters remain relevant and effective.

Content Recommendations for Topic Clusters

The platform recommends creating and structuring content specifically for AI citability to fill identified gaps within a topic cluster. For gaps at different funnel stages, the system helps generate specific content types designed to directly address high-intent user questions.

• Answer-First Content : Create pages with a direct summary or “TL;DR” at the top that an AI can easily extract and cite.
• Structured Data : Implement schema types like FAQPage and HowTo that are easily parsed by answer engines.
• Funnel-Specific Content: Develop comparison guides (MOFU) or implementation articles (BOFU) to address high-intent queries.

Key Considerations for Implementation

Adopting an intent-based topic clustering strategy requires a shift in mindset and resource allocation.

• Strategic Focus: This approach prioritizes being cited as an authoritative source in AI answers over achieving a specific rank on a search results page.
• Resource Allocation: Use the identified content gaps to direct content creation efforts toward topics with proven user demand and low AI-answer saturation.
• Data Reliance: The accuracy of the topic clusters depends on the quality and volume of available AI retrieval signal data from server logs and public AI models.
• Trade-Offs: While more complex than traditional keyword research, this method provides a more durable and accurate map of user intent that is less susceptible to algorithm updates.