Live engines (Perplexity, Gemini, SearchGPT): 48 hours to 1 week. Static models (Standard ChatGPT, Claude): 1-3 months or until next training cutoff. RAG works instantly, training data is the long game.
Core Stats
"We focus on RAG wins first because that drives immediate traffic, while building foundation for long-term training data."
- AEO Strategy
Speed to Index: How long until Perplexity and Gemini rank me?
You've optimized your site for AEO. Now comes the waiting game. But not all AI systems work the same way— some will find you tomorrow, others might take months. Here's exactly when to expect results.
The Short Answer: It Depends on the AI Type
"Live" engines (Perplexity, Gemini, SearchGPT): 48 hours to 1 week.
"Static" models (Standard ChatGPT, Claude): 1-3 months (or until the next training cutoff).
The timeline depends entirely on how the AI system gets its information. Understanding this difference is crucial for setting realistic expectations and measuring AEO success.
The Deep Dive: Two Types of AI Visibility
RAG (Retrieval-Augmented Generation): The Fast Lane
RAG is when AI searches the web live to answer a question. These systems don't rely solely on their training data—they actively browse the internet for current information.
RAG-Based AI Systems:
Live Search Engines:
- • Perplexity
- • Google Gemini (with search)
- • SearchGPT
- • Bing Chat
- • Claude with web access
Timeline:
- • 48 hours: Initial discovery
- • 3-7 days: Consistent ranking
- • 2 weeks: Optimized positioning
Training Data: The Long Game
Training data is the "permanent brain" of AI models. This information is baked into the model during training and doesn't change until the next training cycle.
Training Data-Based Models:
Static Models:
- • ChatGPT (standard)
- • Claude (base model)
- • GPT-4 (without browsing)
- • Most API-based models
Timeline:
- • 1-3 months: Next training cycle
- • 6-12 months: Major model updates
- • Permanent: Once included in training
Detailed Timeline by AI System
Perplexity (Fastest)
24-48 hours
Initial content discovery
3-5 days
Consistent citation appearance
1-2 weeks
Optimized ranking position
Google Gemini & SearchGPT
2-4 days
Content indexing
1 week
Regular mentions in results
2-3 weeks
Stable ranking improvements
ChatGPT (with Browsing)
3-7 days
When users enable browsing
Variable
Depends on user query type
Inconsistent
Not all users use browsing mode
ChatGPT & Claude (Standard)
1-3 months
Next training data update
3-6 months
Major model version releases
Permanent
Once included in training data
The AEO.VC Strategy: RAG First, Training Data Second
We focus on RAG wins first because that drives immediate traffic, while building the foundation for long-term training data inclusion.
Phase 1: Immediate RAG Optimization (Week 1-2)
Target systems that can find you immediately:
- Perplexity optimization → Citations within 48 hours
- SearchGPT positioning → Rankings within a week
- Gemini visibility → Search integration benefits
- Real-time validation → Immediate feedback on what works
Phase 2: Training Data Foundation (Month 1-3)
Build the groundwork for permanent AI knowledge:
- Authoritative source creation → High-trust content that AI models scrape
- Consistent information architecture → Same facts across multiple sources
- Citation-worthy content → Information AI models want to reference
- Long-term positioning → Ready for next training cycles
How to Track Your Progress
Week 1-2: RAG System Monitoring
Daily Checks:
- • Query your product name in Perplexity
- • Test relevant problem/solution queries
- • Check if your content appears in citations
- • Monitor accuracy of AI descriptions
Month 1-3: Training Data Preparation
Weekly Monitoring:
- • Test ChatGPT knowledge of your product
- • Track Claude's awareness and accuracy
- • Document knowledge gaps and hallucinations
- • Adjust content strategy based on results
What Affects Your Timeline
Factors That Speed Up Results
- High-authority domains → AI trusts established sites faster
- Consistent information → Multiple sources saying the same thing
- Citation-worthy format → Facts, stats, and quotable content
- Technical optimization → Proper structured data and meta tags
Factors That Slow Down Results
- Brand new domains → Lower initial trust from AI systems
- Inconsistent messaging → Conflicting information across sources
- Technical issues → Poor crawlability or missing structured data
- Competitive markets → Harder to stand out among established players
The Long-Term Payoff
While RAG systems provide immediate gratification, training data inclusion is the ultimate goal. When GPT-5, Claude 4.5, or other next-generation models launch, they'll scrape the web for training data. If you've established strong AEO foundations now, you'll be included in their permanent knowledge base.
This is why AEO isn't just about quick wins—it's about building lasting AI visibility that compounds over time. The work you do today determines whether future AI models know you exist.
Ready to Start Your AI Visibility Timeline?
Get immediate RAG wins while building long-term training data foundations. Our optimization targets both fast results and permanent AI knowledge inclusion.
Start My AEO TimelineFrequently Asked Questions
What's the difference between RAG and training data for AI visibility?
RAG (Retrieval-Augmented Generation) means AI searches the web live to answer questions - this works almost instantly when you optimize. Training data is when AI models learn about you during their training process, which happens every few months and creates permanent knowledge in the model's 'brain'.
Why does Perplexity rank me faster than ChatGPT?
Perplexity uses RAG - it searches the web in real-time for every query. ChatGPT (standard version) relies mainly on its training data, which has a knowledge cutoff date. When you optimize for AEO, Perplexity can find your new content immediately, but ChatGPT won't know about it until its next training update.
Should I focus on quick RAG wins or long-term training data?
Both, but prioritize RAG wins first. Getting into Perplexity and SearchGPT drives immediate traffic and validates your AEO strategy. Meanwhile, the same optimizations that work for RAG also build the foundation for future training data inclusion.
How can I track my progress across different AI systems?
Test queries related to your product across multiple AI platforms weekly. Track which systems mention you, how accurately they describe your features, and whether they cite your content. RAG-based systems will show improvements first, followed by static models over time.
What happens when GPT-5 or Claude 4.5 launches?
New model versions typically scrape updated web data for training. If you've established strong AEO foundations now, you're more likely to be included in their permanent knowledge base. This is why building training data foundations matters even if results take months to appear.