Generative Engine Optimization (GEO) / AI Search Optimization (AIO) ChatGPT, Perplexity, Gemini 3-4 K INR
Budget / Salary₹1,500–12,500
TypeFreelance project
LocationRemote
Posted40 minutes ago
Project cost 3-4 K INR
My brand needs to surface more prominently inside ChatGPT, Perplexity, and Google AI Overviews. I want a specialist who understands Generative Engine Optimization—entity-based SEO, semantic search principles, structured data, and digital PR—to close the visibility gaps I have already noticed across these LLM-powered environments.
Here is the workflow I would like you to follow:
1. Research & Audit
• Map how my core brand, products, and content are currently interpreted by each of the three engines above.
• Flag missing or misaligned entities, schema issues, and other obstacles that keep my material from being cited or summarized correctly.
2. Suggestions & Strategy
• Translate the audit into an actionable blueprint focused on AI retrieval mechanics, not traditional keyword stuffing or bulk link tactics.
• Prioritize fixes by expected impact on prompt results and answer boxes.
3. Approval
• Walk me through the findings so we can agree on what moves forward. No change goes live without my sign-off.
4. Execution
• Implement only the approved optimizations, whether that means refining schema markup, reshaping on-page copy to strengthen entity relationships, or securing topical mentions through targeted digital PR.
• Provide before-and-after snapshots so I can track the improvement in generative results.
There is no immediate deadline, so quality and precision matter more than speed. If you have proven success in GEO/AIO and can demonstrate how your work directly influenced AI summaries or answer generation, I would like to see relevant examples along with your approach.
My brand needs to surface more prominently inside ChatGPT, Perplexity, and Google AI Overviews. I want a specialist who understands Generative Engine Optimization—entity-based SEO, semantic search principles, structured data, and digital PR—to close the visibility gaps I have already noticed across these LLM-powered environments.
Here is the workflow I would like you to follow:
1. Research & Audit
• Map how my core brand, products, and content are currently interpreted by each of the three engines above.
• Flag missing or misaligned entities, schema issues, and other obstacles that keep my material from being cited or summarized correctly.
2. Suggestions & Strategy
• Translate the audit into an actionable blueprint focused on AI retrieval mechanics, not traditional keyword stuffing or bulk link tactics.
• Prioritize fixes by expected impact on prompt results and answer boxes.
3. Approval
• Walk me through the findings so we can agree on what moves forward. No change goes live without my sign-off.
4. Execution
• Implement only the approved optimizations, whether that means refining schema markup, reshaping on-page copy to strengthen entity relationships, or securing topical mentions through targeted digital PR.
• Provide before-and-after snapshots so I can track the improvement in generative results.
There is no immediate deadline, so quality and precision matter more than speed. If you have proven success in GEO/AIO and can demonstrate how your work directly influenced AI summaries or answer generation, I would like to see relevant examples along with your approach.
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