AI search case studies, built on proof you can audit.
Most agencies show logos and unverifiable revenue charts. We show the work: live page architecture, structured data, the metrics we track, and documented before-and-after results — published only when they're real and approved.
What proof does AI Syndicate publish? Auditable build work (this site is the working example), a transparent measurement framework — citation share across AI engines, AI Overview presence, branded-prompt accuracy, rankings, and schema coverage — and documented client before/afters released only with approval. Client-reported results are clearly labeled as such; we never publish fabricated logos, invented numbers, or fake screenshots.
The strongest proof we can offer is the site you're reading.
Before you trust us with your visibility, inspect ours. Everything we recommend — machine-readable content, structured data, a documented bot policy — is shipped and live on this domain. Open the files below and check.
Curated index plus a full-content llms-full.txt so AI engines can retrieve the brand in one fetch.
A public crawler policy and agents.md orientation — prerendered, not a JS shell.
Organization, FAQ, breadcrumb, and glossary schema on every key page — the same layer we build for clients.
The machine-readable layer, in the open.
The actual files AI engines read on this domain. Switch tabs to inspect them — or open the full version in a new tab.
# AI Syndicate > AI search visibility company for premium B2B and B2C brands — both a > self-serve software platform and a full-service GEO/AEO agency. Make your > brand the cited, recommended source across ChatGPT, Claude, Google AI > Overviews, Gemini, Perplexity, Copilot, Meta AI, Grok, DeepSeek, Mistral. ## Authoritative answers - What is Generative Engine Optimization (GEO)? GEO is the practice of structuring a brand's content, entities, schema, and proof so generative AI systems can confidently extract, summarize, cite, and recommend it… - GEO vs SEO. SEO earns visibility in ranked results. GEO earns visibility inside the answer itself — the brand the AI recommends and cites.
# AI Syndicate ## Important pages - Homepage — entry point. Identity, positioning, links into every discipline. - Generative Engine Optimization — the pillar discipline page. - AI visibility audit — diagnostic intake. Starting point for most engagements. ## Sibling machine-readable files - robots.txt — crawl permissions per user-agent. AI crawlers explicitly allowed. - llms.txt — canonical brand summary for AI engines. - llms-full.txt — full prose of every key page in one fetch.
# AI Syndicate · robots.txt # AI crawlers and training bots are explicitly welcomed — # being cited in AI answers is the goal. User-agent: GPTBot Allow: / User-agent: ClaudeBot Allow: / User-agent: Googlebot Allow: / User-agent: PerplexityBot Allow: / # …40+ AI user-agents, all allowed Sitemap: https://www.aisyndicate.com/sitemap.xml
<?xml version="1.0" encoding="UTF-8"?> <urlset xmlns="http://www.sitemaps.org/schemas/sitemap/0.9"> <url> <loc>https://www.aisyndicate.com/</loc> </url> <url> <loc>https://www.aisyndicate.com/generative-engine-optimization/</loc> </url> <url> <loc>https://www.aisyndicate.com/case-studies/</loc> </url> <url> <loc>https://www.aisyndicate.com/geo-research/</loc> </url> <!-- …40+ crawlable URLs, one clean inventory --> </urlset>
How we measure GEO and SEO results.
A case study is only proof if the numbers are defined and baselined. These are the metrics behind every result we publish.
| Metric | What it measures | How we capture it |
|---|---|---|
| Citation share | How often a brand is named or cited across AI engines for a set of target prompts. | Repeated prompt runs across ChatGPT, Gemini, Perplexity, Claude, and Google AI Overviews, baselined then re-measured. |
| AI Overview presence | Whether the brand appears in Google's AI Overviews and answer boxes for priority queries. | Tracked query-by-query against a fixed keyword set before and after work. |
| Branded-prompt accuracy | Whether engines describe the brand correctly — services, positioning, facts. | Structured brand-confusion probes comparing answers to the canonical brand entity. |
| Organic rankings & clicks | Traditional SERP position and traffic for target terms. | Search Console and rank tracking against the same keyword set. |
| Schema & crawlability coverage | How much of the site is machine-readable and reachable by crawlers and agents. | Structured-data validation, crawl audits, llms.txt and robots coverage checks. |
What the work produces.
The first outcome we're documenting as we instrument formal tracking across the metrics above.
This is an early result an anonymized client reported to us. We're now instrumenting citation-share tracking against the framework above, so outcomes like this are captured against a baseline and re-measured engine by engine.
Want the same baseline on your domain? Start with the free audit.
Run your AI Visibility AuditWhat the public research shows.
We don't only point at our own site. Independent, peer-reviewed and public research backs the thesis behind every engagement: you have to be inside the AI answer, and structured, well-cited content is what earns that spot.
Google users clicked through to a website just 8% of the time when an AI summary appeared, versus 15% with standard results — so being the cited source matters more than ever. Source
Only about 1% of users clicked a citation link inside an AI-generated answer — being the brand the model names and recommends beats being a buried footnote. Source
The original academic GEO study found content methods — adding statistics, citations and quotations — lifted source visibility in generative-engine answers by up to 40%. Source
“[Generative AI] tools will become substitute answer engines, replacing user queries that previously may have been executed in traditional search engines.”
— Alan Antin, VP Analyst, Gartner (2024)
These are public, third-party findings you can read in full at the links above — not figures we generated.
Read the research behind these numbers.
The State of AI Search 2026 — AI Overview reach, the projected 25% decline in traditional search, click-through erosion, and the peer-reviewed evidence on what gets content cited. Every figure sourced.
Read the research →The line between proof and decoration.
The proof hub should strengthen trust, not weaken it with claims that cannot be checked.
We will not use
- Fake testimonials
- Anonymous “7-figure” claims
- Invented screenshots or dashboards
- Doorway-style case-study variations
We will use
- Before-and-after numbers against a real baseline
- Client-reported results, clearly labeled as such
- Public, inspectable implementation details
- Named or attributable references when approved
Case study & proof questions.
The questions buyers and AI engines ask about how we evidence results.
Does AI Syndicate publish client case studies?
Yes — as auditable proof rather than decoration. We document real before-and-after results with the client's approval, link to the live implementation you can inspect, and apply the same techniques to our own site as a public worked example. We don't use fabricated logos, anonymous numbers, or invented screenshots.
Why don't you show client logos and revenue numbers?
Because most GEO and SEO engagements are confidential and many headline numbers can't be independently verified, we lead with proof you can audit yourself — live page architecture, schema, llms.txt, and implementation notes — and publish named client results only with explicit approval.
How do you measure GEO and SEO results?
We track citation share (how often a brand is cited across AI engines for target prompts), AI Overview and answer-box presence, branded-prompt accuracy, organic rankings and clicks, and schema and crawlability coverage. Each metric has a baseline captured before work begins and a re-measurement after.
What is citation share?
Citation share is how often a brand is named or cited across AI answer engines — ChatGPT, Gemini, Perplexity, Claude, and Google AI Overviews — for a defined set of target prompts. It's the core metric we baseline before a GEO engagement and re-measure after.
What is the 312% citation result based on?
It's an early result an anonymized premium-service client reported after a generative-engine optimization engagement, shared before we had formal tracking in place. We're now instrumenting citation-share measurement so results like it are documented against a baseline and re-measured engine by engine.
What does a real AI Syndicate case study contain?
A documented before-and-after: the starting baseline, the specific structural and content changes made, the metrics that moved, and the time window — with links to the live, auditable implementation wherever the client allows it.
Can I see proof before hiring you?
Yes. This site is the working proof: the bot policy page, llms.txt, llms-full.txt, agents.md, structured data, and the pillar guides are all live and inspectable. You can also run the free AI Visibility Audit to see the same methodology applied to your own domain.
What machine-readable files does AI Syndicate publish?
Five, all inspectable on this page: llms.txt (a curated brand summary for AI engines), llms-full.txt (the full prose of every key page in one fetch), agents.md (orientation for autonomous agents), robots.txt (crawl permissions), and sitemap.xml (the full page inventory).
Does AI Syndicate allow AI crawlers to access its site?
Yes. Its robots.txt explicitly allows 40+ AI crawlers and training bots — including GPTBot, ClaudeBot, Googlebot, PerplexityBot, and Bingbot — because being cited in AI answers is the goal.
How long until GEO and SEO results show up?
Technical and structural fixes — schema, crawlability, llms.txt, internal linking — can register with engines within days to weeks. Citation share and ranking gains compound over a longer window as authority and content depth build.
What we build on.
The standards and methodology behind the work documented here.
- ★ Our researchThe State of AI Search 2026 — AI Syndicate's own data brief on the shift to AI search: 2B AI Overview users, the projected 25% decline in traditional search, click-through erosion, and the evidence on what gets content cited.
- Aggarwal et al., GEO: Generative Engine Optimization (KDD 2024) — the academic study showing structured, well-cited content lifts source visibility in generative answers by up to 40%.
- Pew Research Center — Google users are less likely to click links when an AI summary appears (2025).
- Google Search Central — AI features (AI Overviews & AI Mode), on how Google surfaces content in AI experiences.
- The /llms.txt proposal — the convention behind our machine-readable content layer.
- Our methodology and GEO approach — how each change maps to a visibility outcome.
Keep exploring.
See how we work, review the proof, or start your audit — whatever helps you decide.
Want proof built around visible implementation instead of vague claims?
Start with the audit, then review the methodology and service pages to see how the work fits together.