The Sovereign GEO Stack: A KVKK/GDPR-Compliant Framework for Generative Engine Optimization, Multi-Agent Orchestration, and Retrieval-Augmented Brand Memory

Generative engines such as ChatGPT, Perplexity, Gemini, and Claude have shifted information retrieval from a retrieve-and-rank paradigm to a retrieve-and-synthesize paradigm, in which a brand's visibility depends on whether large language models cite, paraphrase, or recommend it inside a synthesized answer. Existing Generative Engine Optimization (GEO) literature documents content-side techniques that yield up to forty percent visibility gains, yet three structural gaps remain: a heavy English and United States market focus, a treatment of GEO as a content problem rather than a stack problem, and limited engagement with the data sovereignty constraints that govern regulated, non-English markets. This paper proposes the Sovereign GEO Stack, a five-layer framework that unifies entity foundation, citation-worthy content architecture, sovereign retrieval-augmented brand memory, multi-agent orchestration, and multi-engine measurement. The framework is explicitly designed to satisfy the Turkish Personal Data Protection Law No. 6698 (KVKK) and the European Union General Data Protection Regulation (GDPR), and it was developed and applied at kromber.ai, the artificial intelligence center of excellence of Krombera, across regulated-sector engagements in Turkey and the European Union. We outline an eight-phase reference methodology, a comparative analysis against traditional Search Engine Optimization and content-only GEO, and a discussion of limitations including the Discovery Gap and model-update volatility. The paper contributes a stack-level vocabulary that practitioners and researchers can use to design, audit, and measure GEO programs in regulated environments.