Before and after, with nothing edited out
This page will fill up slowly, because we publish measurements, not testimonials. Here is what a case looks like when it lands here, and what exists today.
What a case looks like here
Answer snapshot
The actual AI answer, before and after, quoted verbatim, so you can judge the change yourself instead of taking our word for it.
Matched-cohort time series
Citation frequency over a fixed query set: the same query × engine pairs, first 14 days versus the latest 14 days. No swapping queries mid-stream to flatter the trend.
Confidence label + confounders
Every figure carries a high / medium / low confidence label and lists the confounders: what else could plausibly explain the movement.
Ourselves
voidX, our own product brand, measured with the exact protocol we sell.
Before offering measurement to anyone else, we pointed it at ourselves: a fixed buyer-question set, three runs per query, every engine we support. That baseline is now our starting line.
Six own-brand experiments, each with control queries, are starting shortly. As they complete, the full before/after data will appear on this page: answer snapshots, matched-cohort time series, confidence labels, dips included.
Where client cases will appear
We don’t have client cases to show yet. When they arrive, three rules apply to every single one:
- Published anonymized: industry and size tier, nothing more.
- Published with the client’s consent, every time.
- Never cherry-picked: the time series will include the weeks the numbers dipped.
Every after starts with a before.
Get your baseline measured now, so there is something to compare against later.
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