AI Visibility Monitoring

Lock the prompt and sampling protocol

Useful AI answer tracking starts with a stable comparison. We define the prompt set, engines, run cadence and available settings around the decisions your business needs to monitor. The protocol records the original wording and identifies which questions test brand facts, service comparisons or broader category discovery.

Changes to the protocol are documented. A new service, revised prompt or different engine context can make a question valuable while also changing the comparison. Keeping those changes visible helps your team understand which observations belong in the same series and which establish a new baseline.

Track presence and factual accuracy separately

A company can appear more often while still being described incorrectly. Each monitoring run distinguishes whether the brand appears, whether important claims agree with approved facts and whether a visible citation points to a relevant source. These measures stay separate in the observation log.

We connect discrepancies to the affected question and source where one is visible. That gives your team a direct route from an answer observation to a useful review task. It also makes factual improvements visible even when mention counts remain unchanged, and prevents a citation count from standing in for a complete assessment of answer quality.

Turn answer changes into the next repair

The recurring digest highlights material answer changes, newly visible citations and inaccuracies that deserve attention. We compare these observations with known website releases or source corrections, preserving dates so your team can examine the sequence. Before-and-after evidence supports investigation without assuming every change came from one intervention.

Each report retains the sample denominator and relevant run context. The resulting view is a consistent record of the chosen questions, useful for choosing the next repair and checking whether a known error recurs. Search visits and qualified inquiries can be reviewed alongside it through a separate performance reporting scope.

How the work is delivered

1. Set the baseline Agree on the prompts, approved business facts and reporting cadence. Record the initial observations and how each measure is classified.
2. Repeat the observations Capture the agreed questions, preserve available run context and flag collection failures or protocol changes for review.
3. Turn changes into decisions Review material shifts, identify the underlying records worth investigating and carry selected actions into the next work cycle.

Example: a corrected company fact stays under review

Illustrative example

After an approved source correction, the team monitors the same founder question across later runs. The log records whether the error recurs, which source appears and the wording of the answer. The comparison gives the team evidence for the next action while keeping unrelated mention trends separate.