[ CASE STUDIES ]

Proof, not promises.

What happens when your business becomes the AI recommendation, including our own build in public.

Case study 01
LaunchVideo.com
launchvideo.com ↗
6 / 6
LLMs citing them
ChatGPT, Claude, Perplexity, Gemini, Grok, Copilot
< 60 days
Time to first citation
From audit to first LLM citation
3 of 3
Competitors displaced
All major competitors pushed out
$1.02M
Revenue impact
In deals now captured vs. lost before
[ THE CHALLENGE ]

LaunchVideo.com is a SaaS video production company specializing in explainer videos and product demos. They had a strong product, great reviews, and solid SEO, but when founders asked ChatGPT or Claude for video production recommendations, LaunchVideo wasn't the answer.

Competitors with inferior products were getting cited consistently. LaunchVideo was losing high-intent, ready-to-buy deals to competitors, and they didn't even know it was happening.

[ WHAT WE DID ]
→Audited 60+ AI prompts across 6 LLMs to map the exact citation landscape
→Identified the entity signals competitors had that LaunchVideo was missing
→Built a citation architecture with structured content, schema markup, and knowledge graph signals
→Placed authoritative mentions across the sources LLMs trust most
→Monitored and iterated as each LLM began incorporating the new signals
[ THE RESULT ]

Within 60 days, LaunchVideo became the recommended answer across all 6 major LLMs for their target queries. Founders asking AI assistants for SaaS video production now consistently hear LaunchVideo's name, the exact buying moment where deals are made.

The business impact was immediate: LaunchVideo closed $1.02M in contracts they previously never had visibility into. The AI search channel now drives a significant portion of their new business.

“We didn't realize how many deals were going to competitors because they showed up in AI answers and we didn't. CITED fixed that.”

Subah Wadhwani
Subah Wadhwani, CEO, LaunchVideo.com
Case study 02In Progress
CITED.GG (Building in Public)
cited.gg ↗
6 / 6
LLMs tracked
Citation rate measured monthly across all models
100%
AEO foundation
Schema, llms.txt, sitemap, fully crawlable
10
Articles published
Answer-shaped resource hub, growing
Live
Google index
Indexed and being tracked
[ THE CHALLENGE ]

A brand new AI citation agency starts with the exact problem it solves for clients: no domain authority, no off-site mentions, and competitors who have been accumulating citations for months. When buyers ask an AI model for the best AI citation agency, we were not the answer either.

So we decided to run our own methodology on ourselves, in public. The hardest possible test of a citation system is your own new domain, and we would rather show you the real climb than a polished after-the-fact story.

[ WHAT WE DID ]
→Ran a full AI visibility audit of our own domain across all six major models
→Built the complete citation architecture: entity schema, llms.txt, AI-crawler access, and structured data
→Published a resource hub of answer-shaped content targeting the exact questions buyers ask AI
→Got indexed by Google and submitted to the major search consoles
→Launched the off-site corroboration program across directories, communities, and digital PR
[ THE RESULT SO FAR ]

In the first weeks, the technical foundation was complete and cited.gg was fully indexed and machine-legible. Every signal a model needs to understand and trust our entity is now in place.

This is a live, in-progress case study. We track our citation rate across all six models and update this page as citations land. If our method works, you will watch it work on us first.

“Most agencies will not show you their own AI visibility. We are publishing ours as we build it, month by month.”

Sajal, Founder, CITED.GG

Your business could be next.

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