looking for a crypto aeo agency?
As a Leading Crypto AEO Agency, we can help improve your AI Visibility.
Most agencies now sell AEO, and in crypto the gap between the teams doing the work and the teams that renamed their SEO retainer is wide.
If you run marketing at an exchange, protocol or fintech, you are probably hearing from both. Here is how I would tell them apart before you sign anything.]]\
Ask how they measure it
A credible agency can show you where you stand before they sell you anything. AEO is about whether your brand gets named and cited when someone asks ChatGPT, Perplexity, Gemini or Google AI Overviews a question in your category, so the baseline has to come from those engines.
That means a defined set of prompts built around real buyer questions, run across several engines and scored for citation share: how often you appear, in what position, with what framing, and next to which competitors. A single screenshot of a model mentioning a client is not measurement. Answers shift between runs and between engines, so look for repeated runs, a stated method, and honesty about where the data is noisy.
Also ask who chooses the prompts. If the agency only tests questions where you already appear, the baseline will flatter you and the report will look great for six months. And if the reporting is still rankings and traffic with “AI” added to the slide title, you are looking at SEO. You may well need that too, but it is not what you are buying here.
Look past your own website
Models do not build their view of your brand from your site alone. In crypto they lean heavily on exchange listings, data aggregators, documentation, Reddit and Quora threads, news coverage, review sites and reference pages like Wikipedia. A plan that is mostly new blog posts on your own domain covers only part of the picture.
Good AEO work starts by mapping which third-party sources the engines actually cite for your category. Then it builds your presence there: consistent entity details across profiles and listings, digital PR that lands in sources the models already trust, and on-site content structured so it can be lifted as an answer, with clear definitions, direct answers near the top and accurate numbers.
Ask which sources they would target for you and why. A specific answer means they have looked. A generic one means they have a template.
Crypto is not a normal vertical
A framework built for SaaS breaks in crypto in three places. The first is accuracy. Models state outdated or wrong facts about fees, supported jurisdictions, token details and past security incidents with total confidence. In a space where users move money based on that information, this is a brand and compliance risk as much as a visibility problem. Your agency should audit what the engines say about you today, flag what is wrong, and know how to fix the sources feeding the error.
The second is regulation. AI visibility does not track regulatory standing. In our MiCA research, Binance appeared in around 76% of AI-generated answers about EU crypto exchanges despite lacking a valid MiCA licence. Models reward the weight of existing coverage, not the cleanest compliance record. If you are licensed, that status needs to be explicit, consistent and easy to cite. An agency that has never looked at data like this in your vertical is working from someone else’s playbook.
The third is audience. Crypto-native buyers and enterprise fintech buyers research differently, and the engines surface different sources for each. An agency that has only worked one side will miss things on the other.
Be wary of guarantees and shortcuts
Nobody controls what a model says, so anyone promising you the top spot in ChatGPT is selling something. The same goes for shortcuts that sound clever: mass-produced AI content, domain or link schemes aimed at models, hidden text written for crawlers. Engines get better at spotting this, and in a regulated vertical a spammy footprint can damage trust signals you spent years building.
Realistic promises sound different. We will baseline your citation share, pick the categories where you can credibly become the cited answer, fix factual errors first, and report honestly on what moved and what did not. Anyone who tells you SEO is dead is also worth a second look, because strong organic rankings and authority still feed the sources the models draw on.
Questions to ask on the first call
Six questions separate the people doing the work from the people selling it, and the specificity of the answers matters more than how polished they sound.
- How do you measure citation share, and can I see a sample report?
- Which engines and prompts would you track for us, and who chooses them?
- Which third-party sources would you target in our category, and why those?
- What have models got wrong about crypto brands you have worked with, and what did you do about it?
- Who actually does the work, and what do I get in the first 30 days?
- How does this sit alongside our SEO, PR and community work?
What good looks like
A good engagement starts with a baseline, picks a small number of categories where you can realistically become the cited answer, fixes factual problems first, builds presence in the sources that matter, and re-measures on a schedule. It should feel closer to research and editing than to a content treadmill.
If you want to see where your brand stands in AI answers today, book a growth plan conversation and we will walk through your category together.