Early results · Anonymized
Early results — and an honest account of what they measure
These are real, early case studies, anonymized. We're a young engagement model, so we'd rather show you a couple of honest data points and explain exactly what they mean than dress up numbers we can't stand behind. Both figures below are audit score movement — a measure of how well a site is structured for AI citation.
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Victor Xu
Founder of SIGNALS · Last updated: June 2026
Case studies
Two clients, two starting points
Here are two early clients and what changed for each. Both figures below are audit-score movement — a measure of how well each site is structured for AI citation, not a traffic or revenue claim.
Client A · Tech / QA founder
SIGNALS audit score: 17 → 68
The score measures how well a site is structured for AI citation across the seven signals in our framework. At 17, the site was failing most of those signals — effectively unreadable and uncitable to the AI engines. After implementation it scored 68, meaning it now passes the majority of them. Share-of-voice tracking in Pulse is ongoing.
Client B · Video producer / creator
SIGNALS audit score: more than doubled
Same framing: the audit score more than doubled after implementation, moving the site from failing most citation signals to passing a clear majority. We're reporting this one as a multiple rather than a precise pair of numbers so the client stays genuinely unidentifiable. Share-of-voice tracking in Pulse is ongoing.
What these numbers are — and aren't
Audit score is an input. Share of voice is the outcome.
In short: the audit score measures how well your site is built for citation, while share of voice measures how often AI actually names you — the first is what we moved here, the second is what we track over time. We want to be precise about this, because it's easy to imply more than the data supports. The audit score is an input metric: it tells you how well a site is structured for AI citation — whether the engines can read it, whether the content is quotable, whether the signals are in place. Moving it from 17 to 68 is real, controllable work, and it's the foundation everything else sits on. But it is not, by itself, proof that an AI named the firm more often.
The outcome metricis share of voice — how often AI engines actually name you versus competitors for the queries you care about — which we track over time in Pulse. That's the number that maps to case flow, and it builds over months. These are early case studies, and that tracking is still ongoing, so we're showing you the input we've moved and being honest that the outcome is still being measured. A strong audit score stacks the odds; it doesn't guarantee a citation, and we won't pretend otherwise.
If you want the mechanics behind the score, our methodology walks through how we evaluate a page, and the AI visibility explainer covers why these signals decide whether AI names your firm. For the bigger picture, see the overview for personal injury firms.
Victor Xu
Founder of SIGNALS
Victor founded SIGNALS, where he built Pulse — the share-of-voice scanner that tracks how often AI engines name your firm — and the seven-signal citation framework behind every audit. Connect on LinkedIn →
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