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AEO & GEO for LLM visibility
We deploy data-driven strategies to ensure your brand is cited and recommended by LLMs including GPT-5, Perplexity, and Gemini. By optimizing entity recognition and structured data, we capture visibility in zero-click search environments and Answer EnginesWe provide AI-native startups with the technical framework needed to maintain authority in generative search results
100% Job Success
Top-Rated Plus
Expert-Vetted
100% Job Success
Expert-Vetted
Top-Rated Plus
Top-Rated Plus
100% Job Success
Expert-Vetted
100% Job Success
Expert-Vetted
Top-Rated Plus
What we're offering
Entity Engineering & Structured Data
With focus on: defining your brand in a language machines understand
Goal is to eliminate ambiguity, ensuring LLMs correctly identify the brand as a "Verified Entity" rather than a generic keyword.
Tools:
Tools:
- Schema.org (JSON-LD)
- Google Knowledge Graph API
- Wikidata
Secret sauce:
Nested Schema Architecture
Implement deeply nested JSON-LD (e.g., Organization linked to specific Service and ProfessionalService tags) to provide the specific "nodes" that AI search engines use to build their knowledge base.
Nested Schema Architecture
Implement deeply nested JSON-LD (e.g., Organization linked to specific Service and ProfessionalService tags) to provide the specific "nodes" that AI search engines use to build their knowledge base.
Visibility Intelligence & LLM Auditing
With focus on: monitoring real-time AI perception and identifying citation gaps across Generative Engines.
Goal is to quantify real-time AI perception and map the specific citation gaps where competitors are gaining recommendation preference.
Goal is to quantify real-time AI perception and map the specific citation gaps where competitors are gaining recommendation preference.
Tools:
- Python
- Bright Data (SERP & LLM APIs)
- n8n
- Gemini/GPT-5 API
Secret sauce:
Automated Polling Workflows
Static SEO audits are obsolete in the generative era. We deploy n8n-orchestrated scripts to poll LLMs daily, tracking real-time fluctuations in brand sentiment and recommendation weights across the generative ecosystem.
Automated Polling Workflows
Static SEO audits are obsolete in the generative era. We deploy n8n-orchestrated scripts to poll LLMs daily, tracking real-time fluctuations in brand sentiment and recommendation weights across the generative ecosystem.
Authority Orchestration & RAG Optimization
With focus on: securing "Primary Source" status by placing high-confidence data within the retrieval path of AI engines
Goal is to secure "Primary Source" status by placing high-confidence data within the retrieval path of AI engines.
Tools:
Goal is to secure "Primary Source" status by placing high-confidence data within the retrieval path of AI engines.
Tools:
- n8n (Automation)
- High-DA Platforms (Reddit, LinkedIn, Quora)
- RAG-optimized content architectures
Delivering "RAG-ready" content structures that LLMs prioritize during live web-search and synthesis phases.
Secret sauce:
Factual Density & Semantic Snippets.
We replace creative prose with high-density, "snippable" semantic units. By formatting technical data into specific fragments that LLMs can easily parse, we increase the probability of your brand being cited as the authoritative source in the final AI answer.
Secret sauce:
Factual Density & Semantic Snippets.
We replace creative prose with high-density, "snippable" semantic units. By formatting technical data into specific fragments that LLMs can easily parse, we increase the probability of your brand being cited as the authoritative source in the final AI answer.
FAQ: Common Questions
SEO optimizes for a list of links (SERP). GEO optimizes for a single, synthesized answer. SEO targets keywords, while GEO targets context and intent. We focus on ensuring the AI understands the facts about your business, not just matching keywords.
No ethical agency can guarantee specific placements in a "black box" model. However, we maximize the probability by ensuring your data is structured, authoritative, and accessible in the formats these models prioritize during training and retrieval.
Unlike SEO which can take months, changes to Knowledge Graphs can propagate relatively quickly, but training data updates for models like GPT-4 take longer. We focus on "Live Web" results (Perplexity, SearchGPT) where impact can be seen in weeks.
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