Over the past six months, my agency Althea Labs has run brand visibility audits on 20+ companies measuring how often AI models (ChatGPT, Gemini, Perplexity, Copilot, Google AI Overviews/AI Mode) actually mention and cite them when developers ask questions in their category.
About half were developer tools companies: Observability, auth, cloud infra, databases, AI hardware. Which means we now have a decent dataset on how LLMs decide which dev tools to recommend.
Some of what we found confirmed what you'd expect, but if you work on a devtools product or you're the developer who somehow inherited "AI visibility" as a side quest there are three key learnings worth knowing.
1. Your documentation is your marketing department now
The best-performing devtools company in our dataset gets mentioned in 41% of AI answers in its category, with remarkably consistent performance across every model. When we broke down WHERE its citations come from, it wasn't the blog. It wasn't press coverage. It was:
- Core documentation
- SDK hubs
- Protocol-level explainers (the "what is SAML vs OIDC" tier of content)
That company earns 17% of all its category citations from its own domain. The median across our whole portfolio is 4.5%.
Once you see it, the mechanism is pretty clear: When a developer asks an LLM "how do I implement X," the model reaches for content that is structured, specific, and extractable. Documentation is accidentally perfect for this. It has clear headings, canonical answers, code blocks, and no marketing language or fluff. A docs page that says exactly what a thing does, in what versions, with what limitations, is one of the most citable objects on the internet.
Another company in the Althea Labs dataset proved this with a sample size of one page. Their owned citation rate is 33%, the highest we've measured, and it's driven almost entirely by a single honest comparison page ("best X databases compared," including competitors) on their own domain. One well-structured, genuinely useful page outperforms years of blog content.
So, what can you actually do with this info? Treat documentation as a discovery surface, not just a support surface. Un-gate your white papers. Add comparison pages that include your competitors and tell the truth. Use spec tables and decision criteria instead of adjectives. If your docs answer the question better than a Medium listicle, the models will eventually notice because right now, across the categories we measured, Medium (37% citation rate), Reddit (32%), YouTube (18%), and yes, our very own DEV.to (16.5%) are eating citations that could point at your domain.
2. Your visibility varies wildly by model, and the gap tells you what's broken
Nobody has one AI visibility number. Everyone has six, and they sometimes disagree.
I want to show you a quick breakdown of a cloud infrastructure company Althea audited. It had 33% visibility on ChatGPT and Gemini, 16% on Perplexity, 18% on Copilot. For an edtech platform, we saw 56% on Gemini and just 9.6% on Perplexity. Across the 12 companies where we captured model-level splits, Perplexity came in below the other models for 10 of them.
The models sit on a spectrum from "training-data-driven" to "search-driven." ChatGPT and Gemini lean on what they learned in training on years of internet history, brand priors, and accumulated reputation. Perplexity barely does; it searches the live web and cites what it finds today.
So the Perplexity gap is diagnostic:
High everywhere, low on Perplexity → You're coasting on brand equity. The models "remember" you, but your current site isn't producing citable pages. That equity erodes every time a model retrains on a web where you're not the source.
Low everywhere, alive on Perplexity → Your content works but you have no entity footprint. The most extreme case in our data: one company registered 0% on ChatGPT, Gemini, Copilot, and both Google AI surfaces — and only existed on Perplexity, because it had a handful of genuinely citable pages and nothing else. That's a single point of failure. One change to Perplexity's retrieval behavior, and their AI visibility goes to literal zero.
Low on Copilot specifically → Pay attention if you sell to enterprises. One database company in our set showed 0% on Copilot and 0.5% on ChatGPT while doing fine on Google surfaces. Copilot is embedded in the exact environment where enterprise buyers do vendor research.
So, what can you actually do with this info? Measure per-model, not aggregate. Then fix the side you're weak on: Search-driven models want citable pages (structure, schema, llms.txt, clean sitemaps so crawlers actually reach your content); training-driven models want entity signals (Wikipedia/Wikidata, Crunchbase, consistent third-party coverage that repeats who you are and what category you're in).
3. If you rebrand without an entity migration, LLMs will keep recommending your old brand
One company in our dataset rebranded in the middle of last year. New name, new domain, redirects done properly, all the traditional SEO boxes checked.
Their old brand name ranks #3 in their category in AI answers and is mentioned 36% of the time. Their new brand name ranks #28 and is mentioned 3.7% of the time.
The retired brand is still 10x more visible than the live one.
Training cutoffs are the reasons for this issue. A model trained before the rebrand has never heard of the new company. It has years of docs, Stack Overflow threads, conference talks, and blog posts about the old name and messaging. Redirects fix HTTP; nothing about a 301 teaches a language model that two entities are the same thing.
And it decays slowly from there. Every quarter until the major models retrain on a web that consistently connects the old name to new one, the recommendations, the "what should I use for X" answers, and the tutorial mentions keep flowing to a brand that no longer exists.
So, what can you actually do if a rename/rebrand is on your roadmap? Treat the entity migration as a launch-critical workstream, not a comms afterthought. Update Wikipedia, Wikidata, Crunchbase, PitchBook, G2, and GitHub org metadata on day one. Get third-party coverage that explicitly states "X, formerly known as Y" so the models learn the equivalence from repetition in authoritative sources. Add Organization schema on your domain declaring the former name. Then monitor model-by-model until the new entity actually takes.
The wide angle view
The most encouraging stat in our whole dataset: The #1 most-visible brand in one category we measured appears in only 17% of AI answers about its own space. The "winner" is absent from 4 out of 5 answers. In dev tools, where documentation culture already produces the exact kind of content LLMs want to cite, that's not a threat. It's the most open distribution channel since early Google and the teams that treat their docs, comparison pages, and community content as citation infrastructure are going to quietly own it.
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