CiteGround / AI-GTM practice / est. 2026
Your buyers ask AI what to buy. Who owns that answer, and are you in it?
CiteGround gets B2B SaaS named in AI answers. Start with GapCheck: a human-verified map of the AI answers in your category, every number carrying its variance band.
Human-verified report within 24 hours. No instant-scan theater.
Buyer prompt 01/05
“best Docker Desktop alternative for Mac”
Named in the answer
grounded on 33 domains · setapp.com / orbstack.dev / portainer.io
Archived run · human-verified
Category reading: public AI answers about a devtools category. No client involved.
Built by a practice native to devtools and fintech infrastructure.
12.1%
of Ahrefs' signups came from AI referrals that were just 0.5% of its traffic, per Ahrefs' own published data. Small traffic, outsized buyers.
~85%
of AI brand mentions trace to third-party sources, per AirOps' off-site signals report. The lever lives off your own site.
0%
our own tool's share of voice in its category at day-0, pre-launch baseline. The playbook we sell starts running on it now; readings publish on the pre-registered schedule.
What is CiteGround?
CiteGround is an AI-GTM practice that builds its own measurement instruments. We get B2B SaaS named in the AI answers buyers ask.
We build the instruments and we run the work. Ads bought the click era; AI answers decide the current one, and most B2B teams have no reading on where they stand. The practice concentrates on devtools and fintech infrastructure, where we are native and funded competitors spread thin.
The bet is measurement honesty. Everything published here shows its noise floor, because a number without one is an opinion in a lab coat.
What is GapCheck?
GapCheck maps who AI engines name for your buyer prompts and where they pull sources from. A human verifies every report within 24 hours.
You give a domain and your category in buyer words. The report comes back with the prompts your buyers actually ask, which brands the AI names on each, an absence verdict per prompt, the grounding domains the answers cite, and the run-to-run variance band on all of it. Concierge on purpose: a human checks every reading before it ships, so the wrong verdict never leaves the building.
That figure is our own property. We run the instrument on ourselves in public before pointing it at anyone else.
Why does every number carry a variance band?
AI answers change run to run. We report the band across repeated runs and per-engine disagreement, never a single fake-precise rank.
Any tool giving you one number is selling noise. The SparkToro/Gumshoe study (2,961 runs, late 2025) put same-list repeatability of a single AI query under 1 percent, which makes "you are #3 in ChatGPT" a coin toss dressed as a metric. Baskets of prompts, repeated runs, rolling windows: that is a reading. One run is an anecdote, and we label our own single-run data exactly that way.
What happens after the report?
One path: read the report, take a 15-minute walkthrough, get a priced roadmap of the exact moves. The roadmap fee credits toward month one.
The report ends with the sequence we would run and its price. If you want it executed, the roadmap and retainer terms are published, band and all. If you take the roadmap and run it yourself, good: the report was still correct.
Where is the proof?
Every claim here traces to a logged, timestamped measurement. Pre-registered experiments, published on schedule, nulls included.
No invented logos, no anonymous "fintech unicorn" case studies. What exists today: a working instrument, a public teardown of our own tool, day 0, and a pre-registered experiment whose results publish on the schedule we committed to in advance, including the misses. When named client results exist, they will be named because the contract bought that right. And the standing test: if we cannot move our own tool's number with the playbook we sell, you should not hire us. Read dates are pre-registered; the series publishes either way.