Capability example · Custom AI Systems
Illustrative build · not a client engagement
Making a company legible to AI search, not just Google
An illustrative build, not a client engagement. It shows how Stallwart approaches generative engine optimization: structuring a company's information so LLMs and AI search systems can retrieve, understand, and attribute it, not just rank it.
- 01Machine-readable facts
- 02llms.txt summary
- 03Entity authority
- 04Retrieval-friendly structure
- 05AI-crawler access
- Domain
- B2B technology and services
- Problem
- AI search systems answer about a company from whatever they can scrape, often wrong or outdated.
- Approach
- Machine-readable content, an llms.txt summary, entity authority, source attribution, retrieval-friendly structure.
The problem
When someone asks an AI assistant about a company, the answer is assembled from whatever the model can retrieve. If the company's own information is unstructured, inconsistent across pages, or absent from machine-readable surfaces, the AI fills the gap with stale or incorrect detail, and there is no clean source for it to attribute. The company does not control how it is described at the moment it matters most.
Traditional SEO does not solve this. Generative engines retrieve and synthesize rather than list links, so the work is making a company's facts and expertise retrievable, consistent, and attributable.
What was assumed
If we rank on Google, AI search will describe us correctly.
What Stallwart asked
What can an LLM actually retrieve about us, and is it consistent and current?
What Stallwart built
This illustrative system makes a company's information retrievable and consistent for generative engines. Core facts, offerings, and expertise are stated in a structured, machine-readable form and kept consistent across the site, so a model retrieves the same answer wherever it looks. A plain-text summary surface, an llms.txt, gives AI systems a canonical, quotable description rather than leaving them to infer one.
Content is organized so a retrieval system can find a clean, attributable passage for a given question, and entity authority is strengthened so the company maps to a single, well-described entity rather than a fuzzy one. Stallwart runs exactly this on its own site: an llms.txt and llms-full.txt, an explicit allowlist for AI crawlers such as GPTBot, ClaudeBot, and PerplexityBot, and structured data that states the entity plainly.
- 01
Give AI a canonical source, not a guess
A machine-readable summary and consistent structured facts give a generative engine one clear thing to retrieve, instead of synthesizing a description from scattered or stale fragments.
- 02
Make passages attributable
Content is structured so a retrieval system can lift a clean passage and point back to its source. Attribution is how a generative answer can credit the company rather than paraphrase it anonymously.
- 03
Strengthen the entity, control the access
Clear entity signals map the company to a single well-described entity, and an explicit crawler allowlist lets the answer engines that matter read the site, the approach Stallwart uses on its own site.
The approach
From scraped guesswork to a controlled, retrievable source.
- 01
Machine-readable facts
Structured, consistent across pages
- 02
llms.txt summary
A canonical description for AI systems
- 03
Entity authority
One well-described entity, disambiguated
- 04
Retrieval-friendly structure
Clean, attributable passages
- 05
AI-crawler access
Explicit allowlist for answer engines
An illustrative architecture. Each layer gives generative engines a cleaner source to retrieve and attribute.
Engineering decisions
Treat llms.txt and structured data as first-class
Generative engines retrieve from machine-readable surfaces. A canonical summary and consistent schema are the most direct way to influence how a company is described.
The trade-off
Maintenance discipline: the machine-readable surfaces must stay in sync with the live site, or they become a new source of wrong answers.
Optimize for retrieval and attribution, not inclusion promises
No one controls whether an LLM includes a company in an answer. The controllable part is being the cleanest, most consistent, most attributable source on the topic.
The trade-off
Honest framing: this improves the odds and the accuracy of being represented, it does not guarantee a citation.
This work connects to
- Custom AI Systems
- Generative engine optimization
- AI search visibility
- Entity authority
- llms.txt and machine-readable content
Frequently asked
What is generative engine optimization (GEO)?
GEO is making a company's information retrievable, consistent, and attributable for generative search systems and LLMs, so that when an AI assistant answers about the company it uses accurate, controlled sources. It relies on machine-readable content, a canonical summary such as llms.txt, strong entity signals, and retrieval-friendly structure.
How is GEO different from SEO and AEO?
SEO targets rankings in a results list and AEO targets extractable answers like snippets. GEO targets how generative engines retrieve and synthesize information about an entity, which puts the emphasis on machine-readable facts, consistency across sources, entity authority, and attribution.
Can you guarantee an AI like ChatGPT or Perplexity will cite us?
No. No optimization guarantees inclusion or citation in a generative answer. GEO improves accuracy and the chance of being represented by making a company the cleanest, most consistent, most attributable source on its topic. Selection remains with the engine.
Is this a real client case study?
No. This is an illustrative capability example of how Stallwart approaches generative engine optimization. It contains no client results and claims no AI citations. Stallwart does apply this to its own site (llms.txt and an AI-crawler allowlist), which is publicly verifiable.
Does AI search describe your company from a guess?
We will make your facts and expertise retrievable and consistent, so generative engines have a clean source to work from.
Last updated: October 13, 2026