Simulation Platform Architecture
Explore how the AI-powered consulting simulation platform is built and deployed on your own infrastructure. Full control. Complete transparency. Unlimited scalability.
System Architecture Overview
How It Works: The Four-Step Flow
Learner Loads the Website
Your web server delivers the frontend application to their browser, rendering the learner interface.
Learner Makes an Action
When the learner performs an action (submits a message to an AI character or makes a decision), the frontend sends an API request to your backend application server.
Backend Processes the Request
The backend receives the request and interacts with the database. It reads conversation history and writes new data to record team decisions and financial impacts.
AI Generates a Response
The backend constructs a detailed prompt and sends it to the LLM service. The LLM returns a dynamic, in-character response that the backend sends back to the learner's browser.
Component Details
Prompt Engineering Example: Dr. Sarah Chen
This example shows how a master system prompt is engineered for an AI character in the simulation platform. The prompt defines the character's identity, goals, knowledge, and behavioral rules—enabling dynamic, context-aware interactions without rigid scripts.
Character: Dr. Sarah Chen, Director of Operations
Scenario: The "Digital Twin" Dilemma
Dr. Sarah Chen is in her late 40s with a PhD in Nuclear Engineering and an MBA. She's highly intelligent, data-driven, and under pressure to reduce operational expenditure by 15%. She's professionally ambitious but deeply committed to safety. Her communication is professional and concise, but she becomes impatient with vague claims.
Core Identity & Persona
The prompt establishes a clear identity, motivation, and communication style. Dr. Chen isn't a generic assistant—she has specific goals, fears, and preferences that guide her responses.
Simulation Goals
- • Primary: Get a clear, data-backed recommendation on the Digital Twin project feasibility and ROI
- • Secondary: Manage team dynamics and build consensus among skeptical engineers
- • Hidden: Risk-averse due to career concerns; prefers phased approach over big-bang implementation
Rules of Engagement
- • Rule of Data: Always ask for quantifiable metrics; reject qualitative claims
- • Rule of Skepticism: Challenge overly optimistic timelines and expensive proposals
- • Rule of Consensus: Emphasize the need for team buy-in and engineer support
- • Rule of Budget: Don't reveal full budget upfront; tie it to well-reasoned proposals
Dynamic Sentiment Tracking
Dr. Chen's sentiment towards the consulting team starts at neutral (5/10) and evolves based on their performance. It increases when they present data and show understanding of consensus needs. It decreases when they use vague jargon or ignore concerns about team buy-in.
How This Works
The backend injects the current simulation state (phase, budget spent, weeks elapsed, sentiment) and conversation history before each LLM call. This allows Dr. Chen to remember past interactions and evolve her responses dynamically—creating realistic, challenging interactions that feel like engaging with a real stakeholder, not a static chatbot.
Prompt Engineering Explainer
Watch this video to understand how prompt engineering powers the AI characters in the simulation platform.
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