From Tool User to System Architect. A complete infrastructure for AI-native growth organized into three operational phases: Intake, Intelligence, and Activation.
How applied diagnostics, research instruments, and governance models align within a unified AI growth system.
The AI Marketing Research Initiative (AIMRI) develops multi-layered systems to support clarity, governance, and strategic coherence in AI-mediated environments.
Each component in this architecture serves a distinct role while remaining structurally aligned. Separation between layers is intentional and preserves clarity between diagnosis, measurement, and governance.
MAHI™ — Applied Diagnostic Framework
Identifies structural risk patterns in live AI-mediated marketing systems. Designed for executives, operators, and AI teams evaluating real-world deployments.
MAHI Index™ — Research Measurement Instrument
Operationalizes select MAHI™ dimensions for empirical analysis and modeling within AIMRI research studies. Used for research, not required for applied diagnosis.
MAD-M™ — Heuristic Governance Model
Models how narrative coherence becomes increasingly fragile under scale, velocity, and governance conditions. Supports scenario reasoning rather than prediction.
MAHI™, the MAHI Index™, and MAD-M™ are components of a unified research system developed by AIMRI, each operating at a different level of abstraction.
Data capture and lead acquisition systems
Behavioral-optimized forms with progressive profiling
AI-powered conversational lead scoring
Strategic content gates with value exchange optimization
Analysis and behavioral insight systems
User journey tracking with motivation mapping
AI-driven audience clustering by behavioral patterns
ML models for conversion probability
Engagement and conversion execution systems
Behavior-triggered sequences with personalization
High-engagement mobile touchpoints
Automated lead nurturing with behavior trees
Experience the frameworks in action. These tools deliver expert-level analysis using the same behavioral science and AI methodologies I deploy with clients.
Map customer beliefs, emotions, and identity layers
Analyze pain points and friction in your customer journey
Calculate return on investment for marketing campaigns, investments, and projects with instant results
Optimize copy and CTAs using behavioral frameworks
AI agent trained on OCEAN personality clusters
Project ROI based on goals and constraints
The technical infrastructure powering AI-native growth systems. Each component serves a specific architectural purpose.
The Structural Knowledge Graph
Used for RAG-based AI retrieval and brand consistency. Acts as the central repository for all brand knowledge, frameworks, and content that AI systems reference for context-aware responses.
The Activation Engine
For deploying complex, automated Behavior Trees and lead qualification. Orchestrates multi-channel engagement sequences triggered by behavioral signals and predictive scoring models.
The High-Performance Foundation
For AEO-ready deployment and speed. Edge-cached infrastructure ensures AI crawlers can index Fact-Dense content instantly, critical for Answer Engine Optimization rankings.
Answers to critical questions about AI Growth Stacks and AI-to-AI Marketing.
Let's discuss how to deploy AI infrastructure that becomes a permanent growth asset.