Optimize module · Use case
Improve Recommendation Share™
You appear in AI answers occasionally, but rarely as the recommendation — usually as a footnote after two competitors. The Optimize module ties each weak prompt to the specific signal causing it, so effort goes where the score will actually move.
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How do you improve Recommendation Share™?
The Optimize module ties each weak prompt to the specific signal causing it, so effort goes where the score will actually move. The work runs in four steps: identify prompts where presence is fine but prominence is low; trace which competitor claim the engine finds more verifiable than yours; publish the specific, checkable counter-fact at the source that matters; re-run and confirm the position moved rather than assuming it did. A measurable rise in how often you are the named recommendation, not just a mention.
Key facts
- Job to be done
- Improve Recommendation Share™
- Platform module
- Optimize
- Typical owner
- Growth and demand generation leads
- Available from
- Growth — $119 / month
- Engines covered
- ChatGPT, Gemini, Claude, Perplexity, Copilot, Grok
- Outcome
- A measurable rise in how often you are the named recommendation, not just a mention.
The problem
You appear in AI answers occasionally, but rarely as the recommendation — usually as a footnote after two competitors.
This is not a reporting inconvenience — it is a measurement gap. AI answers are generated fresh each time and vary between sessions, so anecdotal checks cannot establish a trend and cannot prove whether anything you changed made a difference.
How Geosystems AI handles it
The Optimize module ties each weak prompt to the specific signal causing it, so effort goes where the score will actually move.
- Identify prompts where Presence is fine but Prominence is low.
- Trace which competitor claim the engine finds more verifiable than yours.
- Publish the specific, checkable counter-fact at the source that matters.
- Re-run and confirm the position moved rather than assuming it did.
What you get
Everything below lands in your private workspace and is exportable as PDF or CSV, so the evidence travels with the decision.
| Output | What it contains |
|---|---|
| Prioritised action list | Ranked by expected GVI™ movement |
| Signal attribution | Which of the four GVI™ signals is holding you back |
| Competitor claim analysis | What rivals state that models find easier to verify |
| Before-and-after verification | Proof the answer changed after the fix |
What good looks like
A measurable rise in how often you are the named recommendation, not just a mention.
The measurement backbone is the same across every use case: GVI™ scores each answer on Presence, Prominence, Attribution, and Accuracy, while Recommendation Share™ places that score against the competitors you name. Both are re-run on a schedule so movement is a trend, not a screenshot.
Improve Recommendation Share™
Start with a free AI Visibility Scan and get the baseline this use case builds on — a GVI™ score across all six AI engines, plus every inaccuracy currently in circulation.