A quick clarification and some context on how the “AI twin” actually works. - Each twin isn’t just a generic chatbot. - It’s grounded in real behavioural data

A quick clarification and some context on how the “AI twin” actually works. - Each twin isn’t just a generic chatbot. - It’s grounded in real behavioural data + psychology frameworks (like MBTI and DISC) that are matched with customer roles and communication patterns. For example: If your real customers tend to be data-driven “analyst” types, the twin reasons and responds that way. If they’re more visionary “driver” types, the twin reacts to emotion and ROI triggers. So instead of random AI answers, you’re getting responses that mirror how your actual buyers think and decide — built from your CRM, LinkedIn, and conversation data. I’m particularly curious how others here would: Combine multiple buyer types into a “composite twin” (like 10 VP Marketing profiles) Add validation loops that make the twin’s reasoning evolve with more data Integrate open-source behavioral models rather than proprietary ones Appreciate all feedback — especially from those who’ve worked on LLM fine-tuning, agent memory, or customer simulation before.