> ## Documentation Index
> Fetch the complete documentation index at: https://learn.orbexa.io/llms.txt
> Use this file to discover all available pages before exploring further.

# AI-SEO optimization case studies

> Real-world AI-SEO optimization case studies — optimization paths for different site types, before-and-after performance data, and lessons.

# Case Studies

## 14.1 Case 1: A Small E-Commerce Store's Three-Week Improvement

**Background**: A small-to-midsize outdoor gear e-commerce store with approximately 500 SKUs, running on WooCommerce, with a domain registered three years ago.

**Initial state**:

* Overall score: 52 (UNRATED)
* V: 55, S: 35, G: 40, T: 50, D: 30
* Had SSL but no DNSSEC, no DMARC
* No Schema.org Product markup
* No llms.txt
* Privacy policy was the default WordPress template

**Optimization timeline**:

| Time          | Action                                        | Dimension Change |
| ------------- | --------------------------------------------- | ---------------- |
| Day 1         | Enabled DNSSEC                                | S: 35 to 42      |
| Day 1         | Added DMARC + SPF                             | S: 42 to 52      |
| Day 2         | Installed RankMath, enabled Product markup    | D: 30 to 45      |
| Day 3         | Created llms.txt                              | D: 45 to 52      |
| End of Week 1 | Rewrote privacy and return policies           | T: 50 to 60      |
| Week 2        | Created agent.json                            | D: 52 to 55      |
| Week 2        | Added Organization markup to homepage         | T: 60 to 64      |
| Week 3        | Optimized Sitemap to include all 500 products | D: 55 to 58      |

**Result**: Overall score rose from 52 to 72 (UNRATED to SILVER)

**Key takeaways**:

* The S dimension has the highest return on investment -- two DNS records delivered a 17-point improvement
* D dimension Schema.org markup was auto-generated by a plugin, saving significant manual effort
* llms.txt took 15 minutes to write but boosted the D dimension by 7 points

## 14.2 Case 2: AI Visibility Optimization for a SaaS Product

**Background**: A B2B SaaS product providing project management tools, built on a Next.js custom site.

**Initial state**:

* Overall score: 48 (UNRATED)
* No structured data of any kind
* Had HTTPS but no DNS security configuration
* No llms.txt
* Policy pages existed but lacked Schema.org markup

**Key optimization points**:

SaaS products differ from e-commerce sites -- they do not need Product markup, but they do need:

1. **Organization markup** -- Structured company information
2. **SoftwareApplication markup** -- Structured product information
3. **FAQ markup** -- Structured frequently asked questions
4. **llms.txt** -- A concise description of product positioning, pricing, and core features

**SaaS llms.txt structure example**:

```markdown theme={null}
# ProductName — One-line positioning

> Providing ... for small and midsize businesses

## Core Features
- Feature 1: Description
- Feature 2: Description

## Pricing
- Free plan: Limitations
- Paid plan: Price and features

## API
- Documentation: Link
- Status page: Link

## Integrations
- Supported integrations: List
```

## 14.3 Case 3: The D-Dimension Gap for Major Brands

**Analysis**: An AI trust score analysis of well-known global brand domains reveals an interesting pattern.

Many large brands score highly in V (identity verification) and G (governance) because they have GLEIF registrations, Wikidata entities, public company verification, and other credentials. However, they often underperform in the D (data quality) dimension.

**Root causes**:

* Large brand websites are typically managed by enterprise CMS platforms, and Schema.org markup is not always complete
* Large brands rarely have llms.txt
* Large brands' robots.txt files may be overly restrictive, blocking AI crawlers

**Implication**: Small and midsize merchants can surpass many large brands in the D dimension. llms.txt, comprehensive Schema.org markup, and AI-friendly robots.txt rules are things large brands frequently overlook but smaller merchants can easily implement.

## 14.4 Optimization Efficiency Comparison by Platform

| Platform              | Difficulty | Automation Level           | Typical Time Investment |
| --------------------- | ---------- | -------------------------- | ----------------------- |
| **Shopify**           | Low        | High (built-in Schema.org) | 2-3 hours               |
| **WooCommerce**       | Medium     | Medium (plugins required)  | 3-5 hours               |
| **Next.js / Custom**  | High       | Low (development required) | 1-2 days                |
| **Wix / Squarespace** | Low        | Medium                     | 2-3 hours               |

## 14.5 Common Failure Patterns

| Failure Reason                | Symptom                                                | Fix                                               |
| ----------------------------- | ------------------------------------------------------ | ------------------------------------------------- |
| Implemented but not validated | Schema.org markup has syntax errors                    | Use validation tools after every change           |
| DNS records overwritten       | DMARC records lost during domain migration             | Check all DNS records immediately after migration |
| SSL certificate expired       | Auto-renewal was not configured                        | Use auto-renewal (e.g., Let's Encrypt)            |
| robots.txt too restrictive    | A newly installed security plugin blocked all crawlers | Check robots.txt after installing any plugin      |
| llms.txt is stale             | Product line has changed but llms.txt was not updated  | Review once a month                               |

***

Congratulations on completing Book 6. You now have a comprehensive understanding of the AI-SEO knowledge framework.

**Recommended next steps**:

* Put it into practice: [Templates and Prompts](/en/templates/index) -- Ready-to-use configuration templates and AI prompts
* Dive deeper into trust scoring: [Book 2: OTR Protocol](/en/book-2/index)
* Learn about commerce protocols: [Book 3: UCP Protocol](/en/book-3/index)
* Look up terms: [Glossary](/en/glossary/index)

***

**More case studies**: [Getting Started Cases](/en/book-1/ch8-case-studies) | [OTR Cases](/en/book-2/ch12-case-studies) | [MCP Cases](/en/book-5/ch10-case-studies)