Why self-track first
An AI visibility tool that doesn't track itself is asking for faith it hasn't earned. So week one of this exercise was simple: create a workspace for anovoxlabs.com exactly the way a new Starter account would — brand, aliases, domain, ten buyer prompts, two engines. No special treatment, no internal switches. Whatever the dashboard showed a customer, it showed us.
What the baseline actually looked like
The first run did what first runs do: it found gaps. Several buyer-shaped prompts returned answers naming larger, older competitors and never mentioning us — which is precisely the experience a new SMB account has on day one. The citation board made it specific instead of demoralizing: which prompts, which engines, which rival sources got cited instead. A vague 'we need AI visibility' became six named gaps.
The loop, week by week
Week two, Aim ranked the six gaps instead of leaving a flat list. Week three, three of them became generated assets — an FAQ page, a comparison page, schema markup — each scored by the pre-publish predictor before shipping. Week four, the runs remeasured. Some fixes moved citations, some didn't, and the dashboard said which was which. That closed loop — measure, rank, fix, prove — is the product's entire promise, and running it on ourselves is how we test whether the promise holds.
What this means for you
Two things. First, the self-study now lives permanently at /case-studies/anovox-on-anovox, with its metrics labeled illustrative and its method fully described — steal the playbook, it's yours. Second, we're recruiting the first real customer study: if you run Anovox for a month with results worth sharing, we'll write it with you, measured numbers only, approved by you before anything publishes. The best proof in this category will always be someone else's numbers.
See this on your own brand
Run the free check — same scoring the dashboard uses, no account needed.
Run free check