The one-call version
That runs this whole chain server-side and returns the measurements together.
Everything below is what it fetches, why, and — the part the tool deliberately
leaves to you — how to read the result.
investigate returns measurements, not a cause. It will tell you ChatGPT fell 38 points while
Gemini held; it will not tell you why, because the reason is usually something outside the data (a
rebrand, a campaign ending, a competitor launch). The interpretation rules below are how you close
that gap.
The chain, step by step
Run these individually when you want to vary the window, filter to a topic, or
drill past what the composite returns.
1. Confirm the drop is real, and date it
A single low day is noise; a step change is an event. What you want from this
step is the date the level changed, because that date is what you match
against your own release, campaign, and site-change history.
2. Isolate which engines moved
This is the highest-signal step. See the interpretation table below.
3. Separate “we fell” from “a competitor rose”
Visibility is absolute; share of voice is relative. They move independently, and
which one moved changes the response entirely.
4. Locate the loss
Topic first, then the prompts inside the weakest topic — that’s the level where
you can actually act.
5. Check whether the sources moved under you
If the domains AI answers cite for your space changed, your ranking may not have
moved at all — the answer did.
How to read it
What this cannot tell you
The data has no view of your rebrand, your campaign calendar, a competitor’s
launch, or a change in how your prompts are worded. A drop that lines up exactly
with one of those is explained by it — check that before treating the numbers as
a content problem.
