Answer engine optimization (AEO) requires that we shift content evaluation from “Is this optimized to rank?” to “Is this likely to be retrieved by an AI model?” This new focus demands an updated approach to QA that’s built on structure, not style.
Our AEO retrievability score evaluates whether pages are structured for AI extraction and citation, not just human readability. Now all our strategists use it to prioritize content updates and improve AI visibility.
This article explains:
- The principles behind our AEO Retrievability Scoring System,
- The evaluation criteria and why they matter,
- The engineering choices we made to ensure quality output,
- What we learned throughout the process,
- How we use our scoring system to optimize for AI-powered search, and
- What’s next for our internal optimization framework.
The Problem: No Way To Assess Content Structure for AI Retrievability
With AI Overviews (AIOs) showing up more often in Search and clients asking why their high-quality pages weren’t being cited, we realized we were missing a vital search visibility signal:
Can a machine easily retrieve and extract this content?
We provided our clients with technical audits, content quality scoring, and rank tracking. But we didn’t have a way to test whether a page:
- Clearly matched the search intent of the associated topic.
- Answered questions in extractable, self-contained chunks that AI tools could retrieve and cite.
We couldn’t evaluate content structure for AI systems, but we wanted to help our clients stay ahead of this evolution in search.
To address this growing client need, we needed a system that could:
- Flag structural elements that reduce citation likelihood.
- Score content the way retrieval models “see” it.
- Help strategists and editors catch retrievability issues before publishing.
Since there were no AEO tools on the market, we built one.
Our Design Principles for Building a Scoring Framework
When we set out to score retrievability, we weren’t just building a tool. We were building a shared language between content strategy and engineering to align how strategists plan, write, and edit content with how AI systems parse and cite it.
We anchored the system on four principles:
1. Reflect How Retrieval Actually Works
LLMs select content based on format, clarity, and semantic alignment, not keyword targeting or backlink volume. Our rubric needed to mirror that behavior and emphasize the signals associated with retrievability.
2. Integrate Into Existing Workflows
Strategists and editors shouldn’t need to learn a new tool or platform. We wanted to make the AEO retrievability scoring process lightweight and compatible with our existing briefs and QA templates.
3. Work for Different Content Types
Since we help our clients create and improve a variety of content types, we wanted our tool to score everything from product collections to pillar pages. The system needed to evaluate a range of content — from instructional guides to concept definitions to product comparisons — regardless of format or intent.
4. Provide Feedback, Not a Final Score
Just like with SEO, there are no definitives with AEO. We didn’t want to structure our tool around achieving a perfect score. Instead, we aimed to make the output directional — to show where to improve, what to prioritize, and whether a page is ready to publish.
The AEO Retrievability Scoring Framework
Once we had our principles, we began building a framework to score content based on the structural elements that matter most for AI retrieval. Our system gives SEO and content strategists a repeatable way to evaluate content structure and identify specific improvements needed for better retrievability.
What We Score With Our Framework
Our scoring framework helps us evaluate five key criteria:
1. Header Alignment (0–5 Points)
- What We Ask: Do the H2s and H3s match real-world search queries and reflect how AI systems interpret topic intent?
- Why: Clear, query-aligned headers help retrieval systems understand content structure and identify the most relevant sections to extract.
2. Summary-First Paragraphs (0–5 Points)
- What We Ask: Does each chunk begin with a clear, standalone answer where the lead sentence makes sense on its own?
- Why: AI systems prioritize content that leads with complete answers rather than building up to conclusions.
3. Lexical Grounding and Clarity (0–5 Points)
- What We Ask: Does the content use core phrases early, avoid vague language, and minimize unclear pronouns?
- Why: Clear lexical grounding reduces ambiguity that can cause retrieval systems to skip sections entirely.
4. Chunk Reusability (0–5 Points)
- What We Ask: Could this block appear in an AI response without edits or explanation?
- Why: This reflects how AI systems extract and present content as standalone excerpts.
5. Internal Linking and Semantic Reinforcement (0–5 Points)
- What We Ask: Do links between related chunks use descriptive anchor text and provide strong coverage across concept clusters?
- Why: Links help AI systems understand topical relationships and signal comprehensive topic coverage to retrieval algorithms.
How We Score Content
We review every section on a page and score it from 0-5 for each criterion:
- 0-1: Major issues that prevent retrievability.
- 2-3: Partial compliance with room for improvement.
- 4-5: Strong performance that supports citation likelihood.
We then combine the results into a weighted score:
- 0–10: High risk of being skipped.
- 11–17: Structurally sound, but retrievability improvements needed.
- 18–25: Retrieval-ready (this is our publishing target).
Our AEO retrievability score isn’t a pass/fail mechanism. We use it in content briefs, QA workflows, and audits to guide structure, prioritize edits, and assess when a page is optimized for AI retrieval systems.
From Concept to Code: Building a Reliable Scoring System
To make the scoring framework usable, we had to think like content strategists and systems engineers. The score couldn’t be theoretical or detached from real workflows. It had to be accurate, scalable, and easy to apply.
Here’s what we prioritized behind the scenes.
1. Parse Rendered HTML, Not Just CMS Fields
We evaluate content based on the final HTML output. This allows us to catch formatting issues that don’t show up in structured data or CMS previews, such as missing headings, nested markup, or JavaScript-dependent content that limit retrievability.
2. Normalize Headings for Query Alignment
We built lightweight natural language processing (NLP)rules that map section headings to known query patterns. This helps us identify mismatches between user phrasing and content structure, even when the intent is similar.
Here’s a simplified version of how we flag short or vague headers:
3. Validate Schema Presence, but Don’t Over-Index on It
Schema markup supports retrievability, but it doesn’t guarantee it. We check for key types like Article, FAQ, and HowTo, but we score based on what’s on the page, not just the metadata.
4. Simulate AI Retrieval With Prompt-Based Tests
We use custom prompts to simulate realistic user questions and mimic what a retrieval system might surface for a given query. Then we check whether the content we’re evaluating produces a clean, standalone answer to the question. We use basic heuristics like this to assess whether a chunk can stand alone. This helps us validate the scoring system from the retrieval layer up.
5. Integrate Scoring Into Strategy Workflows
Instead of building a separate interface, we embedded the score directly into our QA and audit tools. Strategists can apply it during content briefs, refreshes, and editorial reviews without needing a separate tool or new workflow. We also built a lightweight diagnostic loop for quick local testing that scores a page’s retrievability from a live URL.
What We Learned While Creating This Framework
Scoring content for retrievability taught us more than we expected. It showed us where good content fails, formatting decisions that matter most, and how traditional SEO signals fall short for AI retrievability.
1. Even Quality Content Must Be Modular
Some of our highest-quality content — topically relevant, well-researched, well-written — still failed retrievability checks. The issue wasn’t the insights. It was the format. Without clear headings, summary-first paragraphs, or chunk-level reuse, retrieval systems didn’t use the content.
2. Formatting, Not Rewriting, Led to Improvement
Small changes made the biggest difference. We increased citation rates by adding headings, splitting paragraphs, and reordering sentences, without changing the core content.
3. Not All Content Types Benefit Equally From Scoring
Definition pages, how-to guides, and FAQs scored higher than other types of content. Longform editorial content, even when excellent, struggled unless it was explicitly structured for reuse. We’re now designing for more formats that treat retrieval as a first-order requirement, not a retrofit.
How We Use Our AEO Retrievability Scoring System Now
Retrievability scoring is built into how we plan, audit, and ship content. It’s not a QA step at the end. It’s part of the strategic layer that shapes net new content from the start.
1. Prioritize Retrievability in Content Audits
When we review high-priority URLs — the pages most important for visibility, conversion, or authority — retrievability is one of the first things we check.
2. Integrate Retrieval Targets in Briefs and Templates
Our content briefs now highlight the need for retrievable sections. We guide writers on how to phrase headers to match user queries, where to create content chunks, and how to begin those chunks so they’re topically relevant and extractable. This builds an AI-optimized structure into the outline and draft stages, not just the final edit.
3. Add Retrievability Checkpoints to QA
We run each page through our scoring rubric alongside standard SEO checks during the final review process. This gives strategists a fast read on whether the page is retrieval-ready or needs a final formatting pass to improve clarity.
4. Correlate Scores With Performance Outcomes
We track how retrievability scores correlate with visibility outcomes, monitoring whether pages get cited after structural updates. If scores remain low and visibility lags, we prioritize a content refresh.
5. Use Scores To Address Client Visibility Questions
When clients ask why certain pages aren’t surfacing in AI Overviews, we show them where the content structure falls short. Our framework allows us to move from speculation to strategy and helps us build trust with our clients.
Next Steps for Improving Our AI-Retrievability Scoring
We built our retrievability scoring system to evolve. As AI retrieval patterns shift and content formats diversify, we can update our framework to stay aligned with those changes.
Here are the areas we’re focusing on next:
1. Using Scoring as a Training Signal
We are integrating retrievability scoring into our internal coaching and client education. It helps us build our strategists’ intuition, guide editorial decisions, and explain visibility changes with greater precision.
2. Retrieval-Aware Content Templates
Our team is designing layout and component libraries that follow the retrieval patterns we’ve observed across different AI systems. These templates will help standardize chunk boundaries, summary placement, and semantic labeling.
3. Real-Time Scoring Inside Our CMS
We are working on native retrievability checks within our authoring tools. The goal is to give content creators immediate feedback during the drafting process, not just in post-publication reviews.
4. Scoring for Multilingual Content
To support global strategies, we are extending the model to evaluate content written in multiple languages. This includes adjustments for linguistic structure, translation effects on chunk clarity, and regional search conventions.
5. Cross-Platform Evaluation
Google isn’t the only retrieval environment that matters. We’re adapting our model to assess retrievability in Bing, Perplexity, and other emerging tools. Each platform has distinct chunk lengths and citation behavior, which requires separate calibration.
The shift from ranking to retrieval demands new tools and new thinking. Our scoring framework equips content teams to optimize for AI systems without disrupting the workflows that drive adoption.
