GEO Academy
Learn how local businesses get found and recommended by AI.
Explore the library
Guides, research, and practical answers for your business.
How AI Is Changing Local Search
What is already changing in local discovery, which AI-search forecasts remain uncertain, and what service brands can measure now.
Coaching and Compensation: Attribution for Teams
When you can tie reviews to individual employees, you get better coaching, recognition, and service-quality diagnostics. Here's how to use them.
Compliant Review Collection Playbook
Google's review policies are strict. Here's how to maximize review collection while staying compliant.
Citation Cleanup for Local and AI Search
How to find and fix material business-information conflicts across Google, Apple, Bing, directories, and relevant industry profiles.
What Is Schema Markup? A Non-Technical Guide
Schema markup helps crawlers understand your business. Here's what it is, why it matters, and what your website needs.
What Is LLMs.txt, and Should Your Business Use One?
LLMs.txt is an emerging Markdown proposal for giving AI tools a concise site guide. Here's what it is, what it is not, and how local businesses can use it.
What Is JSON-LD? The Technical Foundation of GEO
JSON-LD is how you give search engines and AI systems clean structured facts about your business. Here's what it is and why it matters.
Does SEO Still Work in the Age of AI Search?
Yes. See which SEO fundamentals still support Google and AI search, what GEO measures separately, and how local businesses should report both.
Review Collection at Point of Service: A 90-Day Playbook
Build a 90-day review system around the service handoff, a personal ask, employee attribution, and customer language your locations can use.
How to Respond to Negative Reviews Professionally
A practical negative-review response process: verify the facts, protect customer privacy, acknowledge real failures, and move resolution offline.
What is Generative Engine Optimization (GEO)?
A practical definition of GEO for local businesses: what to measure, which public sources to improve, and which AI-ranking claims to avoid.
Put what you learn into practice.
See how your business appears in AI recommendations.