Every AI answer has a source.
Become the source.
AISOP optimizes enterprise brands to be retrieved, cited, and recommended across ChatGPT, Perplexity, Gemini, Claude, and Google AI Overviews.
ChatGPT
Google Gemini
Claude
Perplexity
Grok
Google AI Overviews
Microsoft Copilot
The next evolution of search visibility.
Traditional SEO ranked web pages by backlinks and keywords. Answer Engine Optimization (AEO) and Generative Engine Optimization (GEO) work differently: instead of chasing rankings, they give AI systems the structured evidence and consistent entity signals needed to recommend your brand directly, inside the answer itself.
- Target AI Engines ChatGPT / Perplexity / Gemini / Claude
- Optimization Method Passage Vector & Entity Alignment
- Core Objective Direct Recommendation & Citation
From analysis to implementation.
While legacy tools only monitor search rankings, AISOP structures your content, schema, and web presence so AI answer engines actively choose your brand.
Built for organizations shaping the future.
As AI becomes the primary interface for discovering products, partners, and expertise, organizations need more than visibility—they need to be understood. AISOP helps companies build the signals that AI systems rely on to retrieve, trust, and recommend them.
- Implementation Model Knowledge Architecture
- Measurable Result Greater AI Recommendation Confidence
Full-service implementation
No complex tools to learn. AISOP pairs proprietary AI visibility tracking with expert implementation to fix your citation gaps.
The Theory of Generative Consensus
Large language models don't retrieve a single source—they synthesize consensus across many independent signals before generating an answer.
AI systems synthesize understanding.
Organizations become recommended not because they publish more content, but because their expertise is consistently corroborated across independent, trusted sources.
AISOP helps organizations strengthen the signals that contribute to that generative consensus.
Systematic Implementation Pipeline
How AISOP turns competitive prompt queries into verified generative citations.
Audit
We run your target buyer prompts across leading AI engines to identify citation gaps and competitor positioning.
Strategy
We map high-yield opportunities: missing entity signals, structured data gaps, and passage authority weaknesses.
Execute
Our team builds the actual assets, structured content, schema, documentation, and external entity signals, and implements them directly wherever we have access.
Validate
Our system re-runs the same prompts on a recurring schedule and flags shifts automatically. Our team interprets what changed, why, and what to adjust next.
The Public Interest Program
Not every representation problem is commercial.
AI systems are increasingly becoming part of how people find information, services, expertise, and help. We believe that organizations serving the public should not be disadvantaged simply because machines fail to understand them accurately.
We select a small number of organizations whose missions could benefit from better AI representation and provide the investigation and implementation at no cost.
If your organization could benefit, tell us why.
Apply →Research & Insights
Research, analysis, and practical insights on AI search, emerging technologies, and the future of digital discovery.
View all insights →If You Rank #1 on Google, Will AI Engines Cite You? (And Why The Answer Is No)
Over 70% of sources cited in ChatGPT search answers do not hold a top 10 Google ranking. Discover why page authority and backlinks no longer guarantee generative AI citations, and how passage-level extraction works.
Read Research Paper →The Boy Who Cried Wolf, and the Brands AI No Longer Believes
Why AI answer engines like ChatGPT, Perplexity, and Gemini trust corroborated evidence over confident claims, and what that means for how brands build credibility.
Read Research Paper →Entity Consensus & The RAG Retrieval Pipeline: How LLMs Synthesize Brand Truth
An in-depth look at how vector databases, passage extraction, and multi-source cross-referencing dictate whether an LLM includes your product in vendor recommendation prompts.
Read Research Paper →Frequently Asked Questions
What is AISOP?
AISOP helps organizations improve how AI systems understand, retrieve, cite, and recommend them. Combining research, entity optimization, structured data, content architecture, and technical implementation, AISOP helps companies strengthen the signals that influence AI-generated answers across platforms including ChatGPT, Gemini, Claude, Perplexity, Microsoft Copilot, Grok, and Google AI Overviews.
How is GEO different from traditional SEO?
Traditional SEO helps webpages rank in search engine results. Generative Engine Optimization helps AI systems understand, trust, and recommend organizations in AI-generated answers. Search engines rank pages. AI systems construct understanding. Organizations increasingly need both.
How do AI systems decide which companies to recommend?
AI systems evaluate many signals rather than relying on a single ranking factor. These include entity consistency, authoritative citations, structured data, topical expertise, semantic clarity, and corroboration across independent sources. Companies are recommended because AI can consistently understand and verify what they do—not simply because they rank highly in traditional search.
What is entity optimization?
Entity optimization is the process of helping AI systems consistently recognize an organization as a distinct, trustworthy entity. It strengthens the connections between a company, its products, expertise, people, and trusted external references, making it easier for AI models to retrieve and accurately represent the organization in generated answers.