AIX Framework – Optimize Complex Systems

I built AIX Framework because causal loop diagrams and behavior-over-time models were stuck in static, academic tools. I wanted something dynamic, scalable, and practical. By combining graph theory, system dynamics, and AI optimization, this tool can generate, simulate, and improve complex systems in real time. It was born from my doctoral research in Strategic Leadership & Foresight and I’m most proud that it can scale from small team models to enterprise-level systems without breaking coherence. I’d love feedback on how useful this is for your own systems thinking and strategy work. User Journey Explore Arrive at site and browse demo systems. Interact with existing causal loop diagrams. Experiment Run optimizations on demo models. Trigger behavior-over-time simulations. Inspect marginal data, tweak variables, and see impacts live. Create Build their own system diagram directly on the platform. Save and refine versions. Conversion Decide to engage more deeply. Submit details that become a lead (name, email, org, system use case). Admin Journey Lead Capture Get notified of new lead through the portal. Access their submitted system data and context. Consultation Schedule interview with lead. Discuss scope, pricing, and expected outcomes. Processing Push client’s system through CLD + BOT optimizer. Validate AI-driven reconfigurations and simulations. Delivery Generate executive report: Before/after diagram comparison. Optimization summary. Behavior-over-time analysis. Specific path and recommended interventions. Engagement Store all client input and session data for continuity. Support follow-up optimization rounds as needed. This gives you two clean funnels: User funnel = demo → experiment → create → lead. Admin funnel = lead → consult → optimize → deliver → retain.