LLM citation analysis
Identify where ChatGPT, Claude, and Perplexity omit your brand. Our real-time vector mapping exposes citation gaps and retrieval blocks across major generative models.
Model citation metrics
We dissect the generative search index into three distinct layers to trace exactly why your brand is recommended or ignored.
Training data
RAG retrieval
Citation weight
We trace references back to core training sets, finding where your documentation was filtered out.
Analyze real-time search queries to see which third-party sources the models pull from.
Measure how often your brand is recommended relative to competitors in direct prompts.
Diagnostic query
Submit your domain to map your brand's citation rate. Our technical team will generate a baseline report detailing your RAG visibility and model recommendation gaps.
Technical FAQ
How do you measure LLM share of voice?
How long does the audit take?
We query model APIs millions of times using structured prompt matrices to map recommendation probability.
Our pipeline maps your vector space and compiles your custom RAG visibility report within three business days.
What is Generative Engine Optimization?
Is our proprietary data safe?
GEO focuses on structuring your brand data so retrieval-augmented generation pipelines can easily parse and trust it.
Yes. We only query public models and analyze public-facing web documentation. No internal data is exposed or ingested.