Watchtower re-runs the AI tape on a schedule, detects when answers drift or competitors gain ground, and alerts you before you lose the recommendation.
AI recommendations change when content, policies, or competitive context changes. Without a baseline, those losses show up late.
Policy edits, theme changes, and catalog updates can quietly remove the structured details AI agents relied on yesterday.
You do not need to break for a competitor to win. They only need to answer the same questions more cleanly than you do.
Without an initial read on the key signals, it is hard to tell whether a future drop came from your storefront, the market, or an AI model shift.
Start with a homepage, top collection, or best-selling product. We capture the public signals worth watching over time.
The scan highlights schema, policy, answer, and comparison details that often change without teams realizing the impact.
Results show which issues deserve a baseline now so future changes are easier to catch and explain.
Run the scan to capture the baseline storefront signals worth monitoring so future drops in visibility are easier to spot and diagnose.
