Most brands that start measuring AI visibility want the same thing first: a number. How visible are we inside ChatGPT? But a number on its own doesn't move anything.
The outdoor-living brand in this story — a company in the terrace and open-air living space — had already invested for years in content, social, and technical SEO. As shoppers began discovering products and brands through AI assistants, a new question surfaced: how visible are we inside ChatGPT and other AI platforms — and what should we actually do about it?
They didn't want another tool that only reports a score. They wanted a system that also told them what to do next. Working with Maya, the answer became a weekly, prioritized action plan — and in the first month AI chat traffic grew roughly 19×, in a channel that had previously been negligible.
At a glance
| Metric | Result |
|---|---|
| AI chat traffic (first month) | ~19× growth |
| Focus platforms | ChatGPT and other AI assistants |
| The unlock | Findings turned into a weekly, prioritized action plan |
| Scope of work | Technical foundation, content strategy, external sources |
The challenge: hundreds of possibilities, limited time
The brand had already done a lot of work across its site and channels. The hard part wasn't effort — it was knowing which of that work actually moved AI visibility, and where to spend a small team's limited time next. Specifically, the team wanted clarity on:
- Which technical gaps should be fixed first?
- Which questions and topics deserve new content?
- How should the brand be positioned on external sources like Reddit, YouTube, and social?
- How do you track whether any of this work is affecting visibility inside AI platforms?
- Where should the team's limited hours actually go?
Choosing the right priorities out of hundreds of possibilities isn't something a dashboard can do on its own.
The solution: turning findings into action plans
Maya analyzed the brand's AI visibility on a regular cadence and turned the findings into concrete, applicable action plans. The work ran in two phases.
Phase 1 — A technical foundation AI models can understand
The first step was making the site easier for AI models to read and quote. That meant reviewing page structure, headings, structured-data usage, and the core areas that help language models parse and understand content cleanly.
Phase 2 — From fan-out questions to a content strategy
With the technical base in place, the work widened into content. Using Maya, the team:
- Analyzed the fan-out questions AI models generate behind the main queries in the brand's space.
- Identified opportunities for new blog, FAQ, social, and video content.
- Tracked high-citation Reddit threads and other external sources that assistants lean on.
- Optimized existing YouTube videos — generating title, description, caption, and question suggestions.
- Monitored new and updated pages for crawl, citation, and visit performance through a watchlist.
- Re-prioritized every week, so the most critical technical and content actions rose to the top.
The single most valuable thing wasn't the data itself — it was the explanation of which step to take next, and why.
Human and AI, working together
Maya's action plans aren't purely automated output. The platform's analysis is combined with what the Maya team has learned across other brands, sector data, and expert review. So instead of generic advice, the team worked on priorities specific to their own brand and its current situation.
As their SEO/GEO consultant, Harun Kuşkondu, put it: "Maya is not another analytics dashboard. It is your AI Visibility Strategist." It doesn't just show the data — it puts the next move in front of you. Execution stays with the team, but as a sparring partner it's genuinely strong.
The result: ~19× AI chat traffic
Before working with Maya, AI chat was a negligible source of traffic for the brand. In the first month of work, AI chat traffic grew roughly 19×.
It would be wrong to attribute that entire lift to a single action. But the timing is an early signal of the technical and content work the brand carried out on AI visibility — and, more importantly, the team can now see why it did each piece of work and where to focus next.
- AI chat traffic grew roughly 19× in the first month — from a channel that was previously negligible.
- Technical and content work became a weekly, prioritized routine rather than a guess.
- The team gained a clear line of sight from finding → action → tracked result.
What made it work — three takeaways
- A score is a starting point, not an answer. The value is in turning visibility findings into a prioritized list of what to fix and publish next.
- Make the site explainable, then expand to content. Clean structure and structured data first; fan-out questions, external sources, and video next.
- Prioritize weekly with a small team. The win for a lean team isn't doing everything — it's doing the few things that matter most, in order.
The bottom line
For this brand, Maya wasn't just a tool that showed where it appeared inside ChatGPT and other AI platforms. It became a working system that turned technical gaps, content opportunities, and external-source visibility into a single operational plan — and grew AI chat traffic roughly 19× in the first month.
Not another dashboard; an AI Visibility Strategist working alongside the team.
Methodology note: Visibility, citation, and traffic signals are measured across a fixed panel of intent-based prompts run on AI assistants, together with sessions attributed to AI-assistant referrers. Reported figures cover the first month of work and cannot be attributed to any single action.