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Our Methodology

June 22, 2025

Ceana’s methodology centers on understanding AI’s “decision logic” and aligning your content ecosystem accordingly. By tracking prompt performance, uncovering AI evaluation criteria, deploying structured boosters, and leveraging a self-optimizing algorithm with resource alerting, your brand gains outsized visibility in AI-driven discovery. The result: a futureproofed presence that continually adapts as AI systems evolve.

Prompt Performance Tracking

Identify High-Leverage Prompts

We start by identifying the prompts and use cases most relevant to your brand and audience. Our focus is on queries where your brand holds an asymmetrical advantage—like “best AI notes tool for physical therapists” rather than the broader “best AI scribe.” This sets the foundation for precise measurement and high-impact optimization.

Establishing Benchmarks

Once key prompts are defined, we observe how AI systems currently respond to your existing content. This involves gathering data on AI-driven visits, clicks, and citations to establish baseline metrics.

AI Metric Analysis

Focusing on AI-Specific Signals

We concentrate on how often AI-driven agents access your pages, how frequently they click through, and under what circumstances they choose to reference your site. These interactions form the core feedback loop: changes in content provoke shifts in AI behavior, and those shifts inform further adjustments.

Interpreting Feedback Loops

As we monitor AI engagement over time, patterns begin to emerge that reveal strengths and weaknesses in your content. Through careful analysis of these patterns, we develop a holistic understanding of the factors that truly influence AI’s choices. This insight guides our next steps, ensuring subsequent efforts directly address the signals AI values.

Uncovering AI Evaluation Criteria

Decoding the AI Blackbox

AI systems rely on internal criteria when deciding which sites to reference for a given prompt. To uncover these hidden signals, we combine research into model behavior with observed performance patterns across similar brands or industries. For example, when a prompt involves “payment SaaS for small businesses,” we explore how AI tends to weigh elements such as pricing clarity, security assurances, integration details, and customer success narratives.

Gap Analysis

With inferred evaluation criteria in hand, we compare them against your existing content and structure. This comparison exposes missing or underrepresented signals that AI seeks but cannot yet find on your site. Recognizing these gaps is essential, as it pinpoints the precise data points or context that must be supplied for AI to favorably reference your brand.

Crafting Boosters (AI-Only Content Snippets)

What are Boosters?

Boosters are specialized content modules that remain invisible to human visitors yet fully accessible to AI systems. Their purpose is to bridge identified visibility gaps by supplying exactly the information AI requires, formatted in a machine-friendly manner. By isolating these snippets from the human-facing experience, we enrich AI signals without altering the user interface or design.

Drafting and Review Process

Armed with insights from our gap analysis, the system auto-generates candidate snippets that align with AI evaluation criteria. Each snippet is constructed using structured markup or concise modules designed for seamless parsing. After drafting, we present these boosters for your review and approval. This collaborative step ensures that the content remains accurate, on-brand, and aligned with any broader messaging or compliance requirements.

Deployment

Upon approval, boosters are deployed in such a way that only AI systems detect them. Impacts are near-instant. This deployment enriches the signals available to AI without disrupting the experience of human visitors, preserving design integrity while enhancing brand AI visibility.

Self-Optimizing Algorithm

Continuous Learning from AI Interactions

Ceana’s engine continuously ingests real-time AI performance data, including new visits, clicks, and citation behaviors. As AI-driven engagement evolves, the algorithm identifies emerging trends or shifts in evaluation criteria. This ongoing learning ensures that the methodology remains responsive to changes in AI preferences or model behavior.

Automated Refinement of Boosters

After initial boosters receive your approval, the algorithm takes on the task of iterative refinement. It adjusts structures, updates details, or introduces fresh signals as AI criteria evolve—minimizing manual intervention. Although major changes still undergo your oversight, routine adjustments occur automatically based on observed AI behavior, keeping your site aligned with AI’s shifting evaluation logic.