# Frameworks for AI Search Visibility — Khadija Zaman

> The frameworks Khadija Zaman uses to score and measure AI search visibility: the R2A Content Framework and its instruments, BERAP, the Retrieval Unit Map and the Representation Parity Map.

Source: https://khadijazaman.com/frameworks/

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# 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.

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Framework20 Aug 2026 · 8 min

## 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 →](/frameworks/r2a/)[

Instrument27 Aug 2026 · 7 min

## 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 →](/frameworks/r2a/berap/)

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](/tools/citeability-checker/) for extractability and the [Schema JSON-LD Generator](/tools/schema-generator/) for entity clarity.

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