How we use AI assistant data to find backlinks, fix our own site and see why competitors get recommended

As a new business, we didn't show up in AI assistants. We build Babel42, an AI visibility tool, and what we could see is what got our competitors mentioned, and better still, what got them recommended. So we're using our own tool on ourselves.

The setup is simple. We create a few different buyer personas, and each one asks the assistants several questions. Then we see who gets recommended and the reason the assistant gave for it. With normal SEO you'd just go after keywords. But no one uses AI search the same way, so picking questions to target is a bit of a guessing game. Running several personas and informing prompts with keyword volume data from search helps.

Very useful for us has been the list of cited sources. We pull the list of sources the assistants cited through our MCP.

Then we remove the obvious ones: Reddit, YouTube, competitors, and the big publications that are out of reach for now. What's left is a list of smaller sites we can actually pitch. The full list ran to 66 pages, which would be a pain to sort by hand. Through the MCP it's a couple of prompts.

Then we look at why the competitors won. We started with the smaller fish in our pond rather than the big names. A few of the reasons the buyers gave:

PingMyBrand won because it was the only option with confirmed agency pricing, $199 a month for up to 10 brands. tutka won on an agency plan at $149 a month with GA4 built in. LLM Metrix won because its case study was the most transparent, with an actual number and timeframe in it. ClayHog won partly because it offered to share anonymised case studies.

We use that information to improve our site, add content thats mentioned regularly, add clarity and it's why are publishing original research.