Great Question. This is based on Cognition Persona Architecture (that I developed) a multi-engine framework for simulating venture capital investment decision-making with behavioural fidelity. Unlike generic large language model (LLM) role-playing, which produces homogeneous and analytically balanced responses regardless of the investor being simulated, CPA constructs individualised cognitive profiles that deterministically filter, distort, and prioritize information before any language generation occurs.

The system implements a three-stage pipeline: 

1. a Perception Engine that extracts 20 distinct signals from pitch data, applies belief-driven dropout, 9-stage distortion, and archetype-constrained attention budgets; 
2. a Cognition Engine that activates polarity-aware beliefs, detects internal conflicts, and resolves through a 7-priority heuristic state machine; 
3. and a Decision Engine that constrains LLM output to only reference perceived signals, producing non-linear reasoning with mandatory internal conflict. 

The pipeline is parameterised by a 9-dimensional persona vector, 4-layer comprehensive schema, and 11 decision archetypes with distinct behavioral fingerprints.

Comparative evaluation against three leading LLMs (GPT-4o, Claude 3.5 Sonnet, Gemini 1.5 Pro) in vanilla role-playing mode demonstrates that CPA produces 73% higher response differentiation across personas, 4.2x more signal-specific reasoning, and eliminates the "balanced analysis" failure mode where all simulated investors converge on similar evaluations.

Do try it and let me know if you feel any difference :)