Customer story
How Chattermill 4x'd its AI search mention rate in months
Customer Experience / SaaS An AI-native customer experience platform went from a 20% to an 80% AI search mention rate in a few months, and started fielding enterprise RFPs from brands who found them through ChatGPT and Gemini.
The most satisfying part has been attracting enterprise brands. They are doing research on tools like ChatGPT and Claude, and we are showing up consistently alongside really established enterprise brands.
The challenge
Chattermill is an AI-native B2B SaaS platform that helps consumer brands analyse customer feedback at scale. By December, Georgi Mirazchiev, Head of Demand Generation, had a decent foundation to build on, but it wasn’t compounding.
- Okay SEO, no system behind it. Chattermill had a reasonable organic presence and was already showing up in some AI searches. What was missing was a repeatable process to work out what was landing, what wasn’t, and what to publish next.
- AI search was a black box. Competitors were being surfaced in AEO results while Chattermill often wasn’t, and there was no clear read on why. As Georgi put it, he didn’t know how to structure the content, what terms to target, how the sections should be laid out, or how many articles to produce and refresh.
- Early wins that wouldn’t scale. There were some successes, but nothing built to run at scale or tie back to pipeline.
“We were doing okay, some early successes, but we weren’t really able to do it at scale or properly delve into what was working and what wasn’t.”
What we changed
We took SEO and AI search on as a managed service, with the work focused on being cited in the searches that matter to Chattermill’s buyers.
| Priority | What it looked like in practice |
|---|---|
| Map the questions that matter | Identified the specific questions and searches Chattermill needed to rank and be mentioned in across AI answers, not vanity keywords. |
| Content built to be cited | Structured and produced content designed to be surfaced in AI recommendations, with a clear cadence for publishing and refreshing. |
| A tight feedback loop | Set up a cadence that made it easy to see what was moving the mention rate and double down on it. |
The outcome
The AI search numbers moved faster than Georgi expected, and the pipeline signal followed close behind.
| Timeline | What landed |
|---|---|
| January | Starting point: roughly a 20% mention rate across relevant AI searches. |
| Around 50% mention rate | Enterprise RFPs started trickling in. |
| May | Mention rate up to 80%, and a major enterprise RFP landing roughly every two weeks. |
The result Georgi values most is the calibre of inbound it pulls in:
- Showing up next to the big names. Enterprise buyers research vendors on tools like ChatGPT, Claude, and Gemini, and Chattermill now appears consistently alongside established enterprise brands.
- RFPs from brands that found them through AI. Last year Chattermill was invited to four or five RFPs. By May this year it was already around 15, from large brands, with most saying they found Chattermill through Google, Gemini, or an AI recommendation.
- The right inbound for the right reasons. The focus on high-intent searches means the companies coming through are a genuine fit, not just volume.
Georgi’s one hesitation going in was timing. He expected SEO to take far longer, based on how slow it usually is to move. It came quicker than he thought, the cadence was set up well, and as he put it, the reservations about how fast results would come were completely unfounded.
“We had reservations about how quickly you can get results. They were completely unfounded. Expectations were exceeded, both on my side and for everyone in the company.”
