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AI Optimization · GEO Actions

What should you change to improve your AI visibility?

Laup turns your AI Visibility data into evidence-backed GEO Actions, ranked by estimated impact and confidence. See the relevant categories, engines, competitor gaps and source evidence with the recommendation.

Laup AI Optimization

Action plan

12 opportunities

Ranked by impact and confidence

AI analysis

Your largest controllable gap is product information: competing pages answer the comparison questions AI retrieves more completely.

All enginesProduct dataContent gapsSource gaps
  1. 92

    Add comparable specifications to priority product pages

    Product data

    Robot vacuums · ChatGPT, Gemini

  2. 84

    Build a buying guide around the questions you are losing

    Content gap

    Robot vacuums · 6 buying questions

  3. 78

    Earn inclusion in the comparisons AI already retrieves

    Source gap

    3 source domains · Copilot, Claude

Illustrative Action plan using the fields and workflow available in Laup.
Inside the product

What you get with Laup AI Optimization.

A prioritized Action plan
Start with the opportunities Laup estimates can matter most, ranked by impact and model confidence.
The case behind every action
Inspect the measured or inferred evidence, relevant categories, buying questions, competitor gaps and sources behind a recommendation.
Category and engine scope
See where the opportunity exists instead of applying the same recommendation across your whole market.
Concrete implementation
Move from a diagnosed gap to specific work on product data, content, sources, feeds and technical signals.
A shared operating queue
Plan, discuss, implement, measure or dismiss actions without losing the evidence that created them.
A plan built from your data
Recommendations come from the answers and evidence Laup measures for you, not a generic GEO checklist.
Inside an action

See the recommendation and the case behind it.

A useful action must explain more than what to do. Laup keeps the diagnosis, evidence, expected mechanism and implementation path together so your team can challenge the reasoning before committing the work.

92

Illustrative GEO Action

Add comparable specifications to priority product pages

Product dataHigh impactHigh confidenceRecommended

Overview

Competing retailers answer the comparison questions in this category with clearer, more complete product information. Laup sees a controllable product-data gap rather than a lack of demand for your brand.

Evidence

Affected scope
Robot vacuums · 6 buying questions
Engines
ChatGPT, Gemini and Copilot
Sources in the answer context
Editorial reviewsComparison sitesYour product pages

Implementation

Add consistent dimensions, runtime, navigation, surface compatibility and included accessories to the priority product pages. Present the fields in visible HTML and use a comparison table on the category page so people and AI systems can evaluate the same facts.

Achievability: High. The required information already exists in the catalogue; the work is making it complete, consistent and directly comparable.

Move to

RecommendedPlannedIn progressMeasuringDone
Illustrative data and presentation, shown to demonstrate the product.
From evidence to action

Recommendations that can show their work.

The Action plan starts with what AI actually said. Models can diagnose and propose, but they do not get to invent the evidence numbers presented to you.

  1. 01

    Measured visibility

    Laup assembles the answers, categories, engines, competitors, sources and technical readiness it has measured for you.

  2. 02

    Independent analysis

    Multiple models examine the same evidence and propose likely root causes and opportunities from different perspectives.

  3. 03

    Verification in code

    References are checked against your data. Affected questions, competitor gaps and source counts are then computed from verified measurements.

  4. 04

    Ranked GEO Actions

    The result is a prioritized plan with evidence strength, confidence, estimated impact and a concrete implementation path.

The work behind the score

GEO is not one change in one place.

AI can draw on learned knowledge, live sources and the information your business publishes. That whole surface is what GEO (Generative Engine Optimization), also called AEO (Answer Engine Optimization), covers. Laup identifies where the evidence points, then turns that diagnosis into work your team can perform.

On your site

Make your offer easier to understand.

Improve product data, comparison content, category landing pages, technical delivery and entity signals where the evidence shows a gap.

Across the sources AI uses

Enter the context behind the answer.

Build a useful presence in the editorial, comparison and community sources that appear around the buying questions you care about.

Across engines and categories

Act where the opportunity exists.

Prioritize the categories, engines and question clusters where a targeted change has a clearer path than a site-wide GEO initiative.

The operating queue

A plan your team can work.

Keep the evidence attached while an opportunity moves from recommendation to implementation and measurement. Park or dismiss work without losing the record.

  1. 01RecommendedRanked by estimated impact
  2. 02PlannedQueued to act on
  3. 03In progressBeing implemented
  4. 04MeasuringWatching the signal
  5. 05DoneShipped
AI Agents

Connect your AI Agents with Laup.

Connect through MCP. Let your agents analyze your data, surface likely root causes, perform actions and verify whether the result changed against the underlying evidence.

AI Optimization

Turn visibility into action.

Build your Action plan from the categories, engines, answers and sources Laup measures for you.

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AI Optimization (GEO) | Laup