AI answers are becoming the first impression—often before a user ever reaches your site. In this aeo case study, we’ll show how The Sol Studio helped a client earn more consistent citations in AI-generated answers by making content easier to retrieve, ground, and quote.
This wasn’t a single “hack.” We focused on reducing citation friction: the common reasons an answer engine can’t confidently use your page as a source.
Table of Contents
- What changed: from rankings to citations
- What did we audit first (and why)?
- AEO case study: which changes moved the needle?
- Prioritization table (use before you write more)
- How did we measure success without guessing?
- What should you do next (and when is AEO a fit)?
- Frequently Asked Questions
- Book a Call: get your site citation-ready
What changed: from rankings to citations
Classic SEO asks: “Can we rank page X for keyword Y?”
AEO (answer engine optimization) adds: “When an AI system generates an answer, is our page easy to use as a source—and does it get cited?”
Two ecosystem updates made this more measurable:
- ChatGPT Search can show clickable sources in results. (help.openai.com)
- Microsoft introduced an AI Performance report in Bing Webmaster Tools (public preview February 10, 2026) showing how publisher content appears across Copilot and AI-generated summaries in Bing. (blogs.bing.com)
Our working rule: citations get more likely when content is (1) discoverable, (2) extractable, and (3) trustworthy enough to ground an answer.
Service overview: AI Visibility services.
What did we audit first (and why)?
Before rewriting anything, we ran an AEO audit focused on retrieval readiness—not just on-page SEO. The goal was to remove blockers that make citations inconsistent even when rankings look “fine.”
1) Indexation + crawlability
If indexation is inconsistent, citations will be inconsistent. We checked:
- Canonicals and duplicate clusters
- Sitemap coverage vs. index coverage
- Thin pages and parameter-driven duplicates
When publish velocity matters, we evaluate IndexNow because it’s designed to notify participating search engines when pages change. (indexnow.org)
2) Information architecture (IA) and “answer placement”
Answer engines tend to cite pages that contain a clean, quotable segment: a definition, steps, comparison, or scoped recommendation.
We flagged pages where:
- Headings were generic (e.g., “Overview”) and didn’t match a user question
- The key answer was buried under long intros
- Multiple intents were mixed on one URL (education + sales + glossary)
3) Evidence + policy alignment
We avoided tactics that read engineered or manipulative. Bing’s webmaster guidelines explicitly warn against behaviors like keyword stuffing and other low-quality practices. (bing.com)
We also validated structured data against Google’s structured data policies—markup must reflect visible content and meet quality requirements. (developers.google.com)
4) Measurement readiness (so iteration is responsible)
We set expectations early: AEO measurement isn’t just clicks.
Baseline checks included:
- Which URLs were already earning citations in AI experiences (via Bing AI Performance)
- Which queries triggered those citations (grounding queries)
- What the cited passages looked like (format, placement, specificity)
Reference: Bing’s AI Performance help page describes reporting and grounding query concepts. (bing.com)
AEO case study: which changes moved the needle?
The theme: make it easy to lift the right chunk, and easy for a human to trust it.
1) Rebuild key pages around “citation blocks”
A citation block is a short, information-dense section that stands on its own without surrounding context. We added or rewrote blocks such as:
- A crisp definition (1–2 sentences)
- “When to choose this” (decision criteria)
- A short comparison (“X vs Y”) with tradeoffs
- A steps section with constraints (who it’s for, prerequisites)
Instead of expanding pages, we trimmed vague paragraphs and moved answers closer to headings. Pages that get cited often include a segment that could be read aloud without extra setup.
2) Build intent-specific supporting pages (not one mega-guide)
Rather than forcing everything into one “ultimate guide,” we built a small cluster:
- One page answering the primary commercial question
- Supporting pages addressing objections and alternatives
Then we cross-linked with descriptive anchors so both people and retrieval systems can land on the best-matching URL fast:
This reduces internal competition while creating multiple entry points for different query shapes.
3) Use structured data to clarify purpose (not as a gimmick)
We used schema where it reduced ambiguity about what a page is and what sections represent.
On FAQs specifically: FAQPage schema still exists, but Google limited FAQ rich results visibility starting in 2023 for many sites. We treat FAQs as a clarity + extraction tool first, and we keep markup aligned with visible content and policy. (developers.google.com)
4) Tighten E-E-A-T signals a reader can verify quickly
“Trust” gets practical when it’s visible on the page. We emphasized:
- Clear author/maintainer model (who wrote it, who updates it)
- Update hygiene when content changes materially
- Links to primary references when stating standards or rules
This doesn’t guarantee citations, but it reduces the chance of being treated as low-confidence.
5) Create one page designed for “grounding queries”
Bing’s AI Performance report surfaces grounding queries—the phrasing used to retrieve content that ends up in AI answers. (bing.com)
We used those patterns to shape one “citation target” page:
- Headings that mirror the query pattern
- A short answer immediately under each heading
- Supporting detail (constraints, exceptions, next steps)
We didn’t chase every query—only the subset tied to revenue and sales conversations.
Prioritization table (use before you write more)
| Deliverable | What it’s for in AEO | When to prioritize | What “good” looks like |
|---|---|---|---|
| Citation-target page (single topic) | Win a narrow set of grounding queries and become a default source | You already have demand and need a reliable cited page | 1–3 crisp answers, strong headings, clear constraints |
| Comparison page | Help AI answer “which should I choose?” prompts | Buyers compare options before booking | Honest pros/cons, decision criteria, clear “best for” |
| FAQ section on a money page | Capture objection questions that show up in AI answers | You get repetitive sales questions | Direct answers in plain terms, aligned to visible content |
| Measurement loop (AI Performance review) | Learn what’s cited and why, then iterate | You’re investing over 60–90 days | URL-level citation trends + grounding query review |
| Technical cleanup (canonicals, sitemaps) | Ensure the right page is eligible for retrieval | Indexation is inconsistent | Stable index coverage and canonical mapping |
How did we measure success without guessing?
AEO fails when measurement is vague. We used three layers:
-
Citation visibility: Monitor citations in Bing’s AI Performance reporting. (blogs.bing.com)
-
Grounding-query alignment: Review grounding queries and ask:
- Are we being retrieved for buyer-intent phrasing?
- Are the cited pages the pages we want cited?
When the wrong URL is cited, it usually signals unclear page purpose, internal competition, or missing “best answer” blocks.
- Business outcomes: Citations aren’t the end goal; they’re an accelerator. We looked for qualitative signals that prospects arrived better informed (e.g., referencing AI summaries), plus shifts in lead quality and clearer first sales calls.
What should you do next (and when is AEO a fit)?
AEO is a good fit when:
- Your category is already being answered inside AI experiences.
- Your sales process benefits when prospects get “pre-sold” by credible explanations.
- You can support content with real expertise, references, and maintenance.
AEO is a poor fit when:
- You can’t maintain pages (stale info breaks trust).
- Your site isn’t reliably indexable yet.
- You need immediate leads tomorrow (AEO compounds; it’s not instant).
If you’re evaluating partners, avoid anyone proposing tactics that violate webmaster guidelines. (bing.com)
To see how we run these sprints end-to-end, start here: AI Visibility services.
Frequently Asked Questions
What makes this an aeo case study instead of a normal SEO case study?
This aeo case study focuses on earning citations inside AI-generated answers, not only rankings and clicks. The workflow prioritizes retrieval-ready structure (clear headings and quotable blocks) and uses citation reporting where available. Success is judged by citation visibility plus downstream commercial signals, not just traffic.
How do you track AI citations in practice?
For Microsoft surfaces, we use Bing Webmaster Tools’ AI Performance reporting to see when pages are cited and which grounding queries triggered retrieval. Then we compare the cited passages to on-page intent and structure. We supplement with business feedback like lead notes and recurring patterns from sales calls.
Does adding FAQ schema guarantee citations or rich results?
No. FAQPage markup can clarify Q&A content for machines, but it doesn’t guarantee citations. Google also limited FAQ rich results visibility starting in 2023 for many sites, so don’t expect enhanced snippets. Use FAQs for clarity and extraction, and keep markup aligned with structured data policies.
How long does it take to see citation movement?
Expect weeks, not days—assuming pages are indexable and the topic is actively answered by AI systems. You typically need time for publishing and indexing, then enough query volume for retrieval patterns to appear. Ship a small cluster, then iterate using citation and grounding-query data.
What’s the fastest way to increase the odds of being cited?
Start with one citation-target page for a high-intent query. Use a question-style heading, answer immediately in a tight paragraph, then add constraints, steps, and references. Make it easy to lift a clean passage, link it from relevant pages, and maintain it as details change.
Book a Call: get your site citation-ready
If you want to replicate the approach in this aeo case study, we’ll tell you quickly whether your site is a good candidate—and what we’d fix first.