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An anchor set is a panel of control brands re-measured continuously. By tracking stable references, you can detect model drift in AI and correct client scores to isolate what reflects a genuine change in visibility.
The problem: models move
When a provider updates its model, the answers — and therefore the scores — can change even though the brand has done nothing. This is model drift. Without a safeguard, that variation would be wrongly attributed to the brand's performance.
The solution: a control panel
The anchor set is a group of reference brands re-measured continuously. If they all move in the same direction at the same time, it is a sign of model drift, not a real change. The client's score can then be corrected accordingly.
In the AGS
The AGS uses an anchor set to separate the signal (the brand's true evolution) from the noise (model drift). It is one of the concrete responses to the limits of the measurement documented in our methodology.
Every question asked to ChatGPT without your name in the answer is a competitor recommended instead of you — measured across 6,820 real AI answers.