AI SEO Methodology at Ferventers

Prompts differ from keywords. Users ask conversational questions rather than typing fragments. This step identifies how audiences phrase questions to AI systems. The process analyzes actual prompts from ChatGPT, Claude, and Gemini. Prompts contain context and qualifiers that keywords lack. Mapping these patterns shows which content structures surface in AI responses. The output is a list of prompt patterns and conversational intents relevant to the content.

This step defines the primary entity, supporting entities, and their relationships. The primary entity is the main subject. Supporting entities are related concepts. Relationships describe how entities connect. For a company, entities include services, locations, and team members. Each entity has attributes and connections to other entities. The output is an entity map showing what the content must establish.

AI systems extract information from structured content. This step uses definition blocks, lists, tables, and headings. Definition blocks place terms and explanations in predictable locations. Lists provide scannable items that AI can parse. Tables enable comparisons. Headings create hierarchy that signals topic changes. The output is a content template specifying structure and formatting.

AI systems evaluate source credibility. This step adds sources, author credentials, methodology references, and case documentation. Sources include external references and internal documentation. Author signals establish expertise through credentials and published work. Methodology references explain how conclusions were reached. The output is a citation inventory listing claims, sources, and author signals.

Schema markup helps AI systems understand content. This step implements Article for documentation, FAQPage for Q&A content, and BreadcrumbList for navigation. Not all content needs extensive schema. Selection depends on content type. Over-marking with irrelevant schemas dilutes signal quality. Some schemas are avoided when they do not apply. The output is a schema specification listing markup types and properties.

Testing validates optimization across platforms. This step queries ChatGPT, Claude, and Gemini with mapped prompts. Results are documented. Success means the source is cited, extracted content is accurate, and responses address the prompt. Testing occurs on multiple platforms rather than one. The output is a test report with prompt-response pairs and citation status.

Information changes. AI systems change. Content requires maintenance. This step establishes freshness cycles, change logs, and versioning. Freshness cycles define review frequency based on topic volatility. Change logs document modifications and rationale. Version tracking maintains update history. The output is an update schedule with review frequency and documentation requirements.