MOLAVI R&D THINK TANK
Research
Reference frameworks and primary research for becoming a citable source in AI search, RAG systems, and knowledge graphs.
Methodology Paper
MAVI: The Molavi AI Visibility Index
Official specification of the Molavi AI Visibility Index (MAVI). Architectural separation of readiness from observed visibility, mathematical weighting, and empirical benchmark protocols.
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2 September 2026
SAGE: Open-Source SEO, AEO and GEO Audit Engine
A direct review of Taqi Molavi’s public repository: a Python engine for technical SEO, entity answers and generative retrieval readiness.
August 27, 2026
What Is a Digital Twin? Architecture and Uses
Taghi Molavi’s research guide to digital twins: definitions, architecture, simulation, AI, security, and lessons from building Hamzad.
August 26, 2026
GEO Methods: Implementation, Measurement and Limits
A grounded guide to GEO implementation: technical access, evidence-led content, entity authority, platform differences, agent readiness, and measurement.
July 30, 2026
VPA: Vector Perturbation Architecture for RAG
A research note introducing VPA: a practical architecture for reducing semantic entropy, tightening RAG chunks, and improving brand citation absorption in AI search.
July 28, 2026
The Molavi GEO Pyramid: A Five-Layer Framework
A white paper defining a five-layer methodology for moving from machine-readable data to zero-prompt agentic mindshare in AI search.
12 October 2026
Who Does AI Recommend? An Iran–Türkiye GEO Benchmark—and the Measurement Traps Behind the Numbers
An analysis of 144 search-enabled API answers in Persian and Turkish: brand visibility, citations, buyer intent, repeat variation and GEO measurement errors.
25 September 2026
How to Get Your Brand Seen in AI Answers: A Practical GEO Guide
A practical GEO guide explaining how brands become easier to retrieve and cite in AI answers, with a plain-English analysis of a real GEO-Scope benchmark.
18 September 2026
AI Agent Reliability in Production: Five Lessons from This Week’s Research
The strongest agent-systems research this week points to one conclusion: production reliability depends less on adding intelligence and more on controlling memory, verification, observability and change.