How to Read AI SEO Case Studies (and Run Your Own Test)

A useful AI SEO case study shows the exact prompts tested, the assistants used, the dates, the starting point and what changed, so you could repeat the test yourself. Many published examples skip these details. Instead of trusting big claims, learn to judge evidence and run a small test on your own business.

Why AI SEO Results Are Hard to Prove

AI answers are not fixed. Ask ChatGPT the same question twice and you may get different brands in a different order. Answers also change when a model is updated, when the assistant searches the web, or when the user’s location or wording changes. That makes “we got this client into ChatGPT” a weak claim on its own. Was it one lucky run, or a steady pattern?

This is why we do not publish dramatic numbers here. Honest evidence in this field is about trends across many prompts and repeated checks, not screenshots.

What a Credible Case Study Includes

• The prompt list. The real questions tested, ideally 20 or more, written the way buyers ask them.
• The assistants and settings. Which tools were used and whether web search was on.
• Repeat runs. Each prompt asked several times, with the share of runs where the brand appeared.
• A clear baseline. Results before any work started, measured the same way.
• The actual work done. Which pages were written, which listings were added, what was fixed.
• A time frame. When each measurement was taken.
• Other factors. Any press coverage, product launch or ad campaign during the same period.

Red Flags to Watch For

1. A single screenshot as “proof”.
2. Percentage gains with no starting number (“visibility up 400%” from one mention to five).
3. Claims of a guaranteed position or a result “within 7 days”.
4. Talk of “inserting” a brand into AI training data or a special partnership with an AI company.
5. No explanation of the work, only the outcome.

If you see these, ask for the raw prompt list and dates. A serious provider will share how they measure.

How to Run Your Own Before-and-After Test

You do not need a case study from someone else. You can create one for your own business in about three months.

1. Write 25 buyer prompts. Mix broad ones (“best project management tool for architects”) with specific ones (“does [your brand] integrate with Xero”).
2. Record a baseline. Ask each prompt three times in ChatGPT, Gemini, Perplexity and Copilot. Note mentions, accuracy and which competitors appear.
3. Pick a few changes. For example, add three answer-first pages, fix inconsistent prices across profiles and get listed on two relevant directories.
4. Change nothing else on purpose. Keep a log of other events, such as a launch or press piece.
5. Re-measure monthly. Use the same prompts, same tools, same method.
6. Compare patterns. Look at how many prompts now include you and whether descriptions are more accurate.

The page on measuring AI visibility and brand mentions explains the metrics in more detail.

What Results Usually Look Like

Improvements tend to show first on narrow, specific prompts where few sources exist, such as a niche service in one city. Broad, crowded prompts move more slowly. Assistants that search the live web, like Perplexity, can reflect new content sooner than knowledge built into a model’s training. Expect gradual change, not a switch flipping.

How Keboost Helps

Keboost gives you the measurement side out of the box. It tracks your prompts across AI models over time, shows where you and competitors appear, and creates and distributes content to address the gaps, so your own before-and-after record builds itself. It does not promise outcomes. Run a free AI visibility check to set your baseline, and browse other notes on AI search optimization examples.