Because the person writing your pages understands what you built. In a category where an AI engine quotes one passage and moves on, that is not a nice-to-have — it is the whole difference between being cited and being paraphrased badly.
Every firm in this market can produce content. Most of them will assign your protocol to a writer who learns what it does from your brief, writes it back to you in their own words, and moves on to a fintech account on Thursday. That process produces pages that read exactly like what they are.
In short
- Six years marketing crypto from inside the market — positioning, go-to-market, launches — not from an agency floor.
- Technical comprehension is the scarce input: perp DEXes, yield mechanics, zero-knowledge proofs, restaking, custody models.
- Proof rather than claims: Velar’s category had no written definition, so Kunzum wrote one and the engines now return it.
- Hands-on and quick — three clients at a time, no account layer, no briefing loop before a paragraph can change.
- The method is checkable before you buy it: 1,500 AI responses and every scoring script, published raw.
The comprehension problem
A perpetual DEX is not an exchange with extra steps. A yield protocol either has a real source of yield or a token subsidy, and buyers can tell which. A zero-knowledge proof is not advanced encryption. Restaking is not staking twice.
These are not pedantic distinctions. They are the exact sentences a buyer doing diligence reads to decide whether you know what you are talking about — and they are the sentences an AI engine lifts when it answers a question about your category. A page that gets the mechanism subtly wrong does not get quoted, or worse, gets quoted being wrong.
Writing them correctly requires understanding the thing, and there is no way to fake it at length. This is why the work does not scale by adding people: the input is comprehension, and comprehension does not delegate.
Test it before you hire anyone
This is not a claim to take on faith — it is a five-minute qualifying call, and it works on Kunzum too. Ask any agency these, and listen for whether the answer is a definition or a deflection.
| Ask any agency | What a right answer sounds like |
|---|---|
| What does a perpetual DEX actually settle? | Funding rates between longs and shorts, continuously, with no expiry — not a spot exchange with leverage bolted on |
| Where does this yield come from? | A named source — fees, lending spread, staking rewards — or an admission that it is currently a token subsidy |
| Why is a zero-knowledge proof not encryption? | Encryption hides data from everyone without the key; a proof convinces a verifier a statement is true while revealing nothing else |
| What would a sceptical buyer in this category attack first? | The specific claim your competitors have already been challenged on |
| What do buyers here call this, in their own words? | The phrase used in Telegram and on calls, which is usually not the phrase on your homepage |
An agency that answers these fluently for your category can probably write for it. One that says it will “get up to speed on the technical side” is telling you the writer will be briefed, and a briefed writer produces briefed writing.
Why this matters more for AI answers than for SEO
In ordinary search, a thin page can still rank on links and domain authority — the reader clicks through and forms their own view. An AI answer removes that step. The engine reads a passage, decides whether it is usable, and either quotes it or reaches for a competitor’s.
Kunzum’s September 2026 index measured what that looks like in practice: across 360 unprompted answers about cross-border money, engines raised crypto 127 times but named an actionable crypto product only 6 times. The category was in the answer. The products were not, because there was nothing specific enough to name.
And being cited is not the same as being described correctly — Liu, Zhang and Liang found that generative search engines frequently state things their own sources do not support. The defence against being misdescribed is a source that is precise enough to be hard to get wrong. That is a writing problem with a technical prerequisite.
Proof rather than claims
Velar. The category had no written definition, so Kunzum wrote one and placed it where the engines read rather than on the client’s homepage. Ask any engine for the world’s first bitcoin perpdex and see what comes back — the check takes ten seconds, and the write-up includes what it does not prove.
RemoteStack. The same method run on a job board with no client and no budget, now pulled into AI answers about remote hiring. It is the control: it demonstrates the method rather than the brand.
kymo. Built in-house because nothing on the market recorded which crawler read what, or which citation actually sent a human. Every client report runs on it.
The research. 1,500 AI responses and 12,518 citations published raw, scoring scripts included, no email gate — including two pre-registered hypotheses that failed and one finding that argues against Kunzum’s own commercial interest. Check the method before paying for it.
Hands-on, and fast enough to matter
Three clients at a time. No account layer, no junior drafting your protocol page, no two-week turnaround because a paragraph needs to go through someone. The person reading your engine results is the person writing the next page, which means the next page is a response to the data rather than to a brief.
Crypto also moves faster than an agency retainer cycle. When your category shifts — a competitor launches, a narrative turns, a regulator speaks — the useful response is a page this week, not a strategy deck next month.
Where this is not the answer
If you need paid media, KOL campaigns, community management or tier-one press, those are other people’s work and four firms are compared here on exactly that basis. If you need a team of twelve, this is structurally not it.
And nobody can guarantee an AI recommendation. In Kunzum’s own index ChatGPT ran a web search on 54% of its answers and Claude on 65%; the rest came from training data nothing published can reach.
If your product is technically complicated and you are tired of explaining it to people who are paid to already know — that is the conversation. Your questions get run across the four engines before the call, so it starts with what they actually say about you.
Sources
- Liu, Zhang and Liang, “Evaluating Verifiability in Generative Search Engines” (2023). arXiv:2304.09848. Checked 2026-09-12.
- Aggarwal and colleagues, “GEO: Generative Engine Optimization” (2023). arXiv:2311.09735. Checked 2026-09-12.
- Google Search Central, “AI features and your website”. Google. Checked 2026-09-12.
Published 2026-09-12. Written by Narender Charan, who runs Kunzum.