Frameworks for AI search visibility
The scoring models I use to find out why a page ranks but never gets cited, and what an AI model already believes about a brand before it retrieves anything.
What frameworks does Khadija Zaman use for AI search visibility?
Khadija Zaman uses the R2A Content Framework, an eight-layer model that scores a page from retrieval to action, and its instruments: the BERAP Map for what AI models recall about a brand, the Retrieval Unit Map and the Representation Parity Map. Each is a practitioner model with an evidence grade on every recommendation, not an official model from any search engine.
The R2A Content Framework: what a page has to get through before anyone acts on it
A practitioner framework for scoring whether AI systems can retrieve, select, cite and act on a page. Eight layers, one hard gate, graded evidence.
Read on →The BERAP Map: measuring what AI models remember about your brand
BERAP probes AI engines with repeated prompts and grades answers against a dated attribute table, producing six scores for brand recall and accuracy.
Read on →Two more R2A instruments are written up next: the Retrieval Unit Map, which sizes and structures the passages a retrieval system can lift from a page, and the Representation Parity Map, which measures the gap between what a page says and what an engine repeats. Both publish in January 2027. Two of the free tools run parts of R2A in the browser: the AEO Citeability Checker for extractability and the Schema JSON-LD Generator for entity clarity.