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. Both sit inside my approach to AI search optimization.
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 the BERAP Map for what AI models recall about a brand. Two more instruments, the Retrieval Unit Map and the Representation Parity Map, publish in January 2027. 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.