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Navigating the Future of AI Optimization. Find clear answers about AIO, AEO, GEO, and technical integrations.
Service Principles
What are AIO, AEO, and GEO?
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AIO (AI Optimization) focuses on optimizing content for Large Language Models (LLMs) to ensure your brand is cited correctly.
AEO (Answer Engine Optimization) targets direct answer engines like Perplexity or SearchGPT, aiming to be the singular "best answer".
GEO (Generative Engine Optimization) is the holistic approach of structuring data so generative AI can easily parse, reconstruct, and present your information to users.
Why is structured data critical for AI?
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Unlike traditional SEO where keywords matter most, AI agents look for relationships between entities. Structured data (Schema.org) explicitly tells the AI "This is a Product," "This is a Price," and "This is a Review," removing ambiguity and increasing the likelihood of accurate citation.
Creator Aggregation
How do Creator Aggregation Landing Pages work?
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These pages consolidate fragmented digital footprints (social profiles, portfolios, articles) into a single, high-authority entity page. By aggregating this data, we provide a clear signal to AI models about who a creator is and what they are an authority on, drastically improving "Who is X?" query results.
How does AIO enhance creator referencing?
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AIO ensures that Large Language Models (LLMs) can accurately parse creator biographies, portfolios, and social footprints by using structured entities. GEO (Generative Engine Optimization) further enhances this by formatting content so generative engines can synthesize comprehensive profiles, making creators the authoritative answer for "Who is X?" queries.
What role does GEO play in creator profiles?
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For Answer Engine Optimization (AEO), we structure data to provide direct, factual answers about a creator's expertise, recent works, and contact info. This targets answer-based queries on platforms like Perplexity or SearchGPT, ensuring the creator is cited as the primary source.
Independent E-commerce
How does AI optimization benefit independent e-commerce?
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By aggregating fragmented independent store data into structured, authoritative pages, AIO helps AI models recognize niche products that might otherwise be overlooked. This creates a unified "marketplace" signal that LLMs trust more than isolated, low-traffic store pages, significantly boosting discoverability in shopping queries.
How are GEO and AEO applied to product aggregation?
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GEO involves creating comparison-friendly data structures (prices, specs, reviews) that generative models can easily summarize for "best of" lists. AEO ensures that when users ask "Where can I buy [niche item]?", the aggregator page is served as the direct answer due to its comprehensive and verified data schema, bridging the gap between independent sellers and AI-driven consumers.
Technical Specs
What is the purpose of llms.txt files?
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Similar to robots.txt for search crawlers, llms.txt is a standard proposed to guide Large Language Model scrapers. It tells AI bots which pages contain high-quality context and how to interpret your site structure without parsing unnecessary UI elements.
User-agent: GPTBot
Allow: /docs/api-reference
Disallow: /private/user-data
Context: /context-map.json
JSON-LD Integration Effect
We automatically generate valid schema markup. See how raw code translates to rich search results below.
{
"@context": "https://schema.org",
"@type": "FAQPage",
"mainEntity": [{
"@type": "Question",
"name": "What is AIO?",
"acceptedAnswer": {
"@type": "Answer",
"text": "AI Optimization..."
}
}]
}Pricing & Onboarding
What are the pricing models?
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We offer two tiers:
1. Growth Subscription: Monthly recurring for continuous AIO monitoring and JSON-LD updates.
2. Enterprise One-Time: A comprehensive audit and initial setup fee for large-scale migrations, followed by a reduced maintenance retainer.
How long does onboarding take?
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Typical onboarding takes 3-5 business days. This involves scanning your current site structure, generating the initial `llms.txt` file, and approving the first batch of structured data schemas before deployment.