Otterly tells you the score. Reaudit changes it.
“We use Otterly for that.”
The short answer
Otterly is a solid AI search monitoring tool, but monitoring is where it stops. Reaudit monitors across more engines and then does the work tracking can’t: a 26-step GTM strategy builder, a GEO-scored content factory for every medium, AI-to-Stripe revenue attribution, code and schema fixes shipped as pull requests, creator outreach, and more. And the whole platform is operable by AI agents through a 180-tool MCP server. Tracking tells you the score; Reaudit is the team that changes it.
The Objection
On a call, an agency owner in New Zealand waved off the demo with four words: we use Otterly for that. Fair. Otterly is a clean, well-built AI search monitoring tool, and if all you want is to watch your mentions, it does the job.
But that framing assumes the job is tracking. Tracking is the dashboard. It tells you where you stand, it does nothing to move you, and it is the easiest 10% of the problem to solve.
Reality Check
Reaudit tracks too, across more engines, so the comparison starts level. The difference is everything that happens after the number lands.
Where a tracker stops at the chart, Reaudit runs the motion that changes it: a 26-step Go-To-Market strategy builder with deep research at every step, a GEO-scored content factory that writes for every medium (articles, FAQs, comparisons, landing pages, social, newsletters), AI-to-Stripe revenue attribution, code and schema fixes shipped as GitHub pull requests, creator discovery and outreach, and more.
And it is all operable by AI agents through a 180-tool MCP server, the largest published marketing MCP. So the same agent that reads your visibility can also write the content and ship the fix. A tracker can’t do that, because a tracker was never built to act.
Strategy
26-step GTM builder with deep research at every step, not just a number on a dashboard.
Content in every medium
GEO-scored articles, FAQs, comparisons, landing pages, social posts, and newsletters.
Revenue attribution
Maps AI bot crawls and AI referrals to real Stripe revenue.
Code and schema fixes
Optimization shipped as GitHub pull requests, plus schema and llms.txt.
MCP-native
180-tool MCP server so AI agents operate the whole platform via natural language.
The Lesson
When a prospect names a point tool, they are usually comparing one feature, not the outcome. The honest response isn’t to argue the feature. It is to widen the frame to the result they actually want.
Tracking is necessary, but visibility you only measure is visibility you don’t improve. The platform that wins is the one that closes the loop: see the gap, decide the strategy, produce the content, ship the fix, and prove the revenue. Whatever tool you use, judge it by how much of that loop it actually closes.
See what your own AI visibility actually looks like.
Before it becomes someone else’s case study. Apply for an AI Visibility Audit and we’ll show you your real mention rate, share of voice, and citations across the AI engines that matter.