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AI Brain MVP v0.1

An intelligent coaching assistant that analyzes youth entrepreneurship conversations and identifies which stage of a 15-stage pedagogical model the learner is currently at, providing structured guidance for entrepreneurship coaches working with young entrepreneurs.

Purpose

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This workflow serves as an AI-powered diagnostic tool for entrepreneurship coaches working with youth. It automatically analyzes conversations with young entrepreneurs and maps them to specific stages in a comprehensive 15-stage pedagogical framework that guides youth from initial psychological activation through to sustained entrepreneurship and mentorship capabilities.

The system helps coaches quickly identify where a learner is in their entrepreneurship journey, understand their current challenges, and access stage-specific guidance including goals, core skills, micro-actions, and readiness checks.

How It Works

  1. Chat Input Reception: The workflow receives a chat message from a user through a chat trigger interface
  2. Data Preparation: Essential information is extracted and organized, including the user's message, session ID, and the complete list of 15 entrepreneurship stages
  3. AI Analysis: An LLM analyzes the conversation against the pedagogical model to determine which specific stage (S1-S15) the youth is currently at
  4. Stage Extraction: The AI's response is parsed to extract the identified stage ID using pattern matching
  5. Stage Details Lookup: The system retrieves comprehensive details for both the current stage and next stage, including goals, core skills, micro-actions, and readiness checks
  6. Structured Output: Returns a complete assessment with stage identification, detailed stage information, and progression guidance

Workflow Diagram

graph TD
    A[When chat message received] --> B[Set:Load Stage Data]
    B --> C[LLM:Thinking Step]
    C --> D[Code:Extract Stage ID]
    D --> E[Code:Get Stage Details]

    F[OpenRouter:Thinking] -.->|AI Model| C

Trigger

Chat Trigger: Activated when a chat message is received through the webhook interface. The trigger captures the user's message content and session ID for processing.

Nodes Used

Node Type Node Name Purpose
Chat Trigger When chat message received Receives incoming chat messages and session data
Set Set:Load Stage Data Prepares stage list, counts, and user message data for processing
LLM Chain LLM:Thinking Step Analyzes conversation to identify current entrepreneurship stage
Language Model OpenRouter:Thinking Provides AI processing power for stage analysis
Code Code:Extract Stage ID Parses AI response to extract stage identifier (S1-S15)
Code Code:Get Stage Details Retrieves comprehensive stage information from internal database

External Services & Credentials Required

  • OpenRouter API: Required for AI language model access
    • Credential: openRouterApi (OpenRouter account 2)
    • Used for analyzing conversations and stage identification

Environment Variables

No environment variables are explicitly configured in this workflow. All configuration is handled through n8n's credential system.

Data Flow

Input: - Chat message content (user's entrepreneurship-related conversation) - Session ID for conversation tracking

Processing: - 15-stage entrepreneurship model framework - AI analysis prompt with pedagogical context - Stage identification and validation logic - Comprehensive stage database lookup

Output: - Current stage ID (S1-S15) - Current stage details (name, lifecycle, goal, core skills, micro-actions, readiness check) - Next stage ID and details (if applicable) - AI thinking process explanation - Original user message for reference

Error Handling

  • Stage ID Validation: If AI fails to identify a valid stage, defaults to S1 (Psychological Activation and Early Agency)
  • Pattern Matching: Uses regex to extract stage IDs with fallback to first valid match found
  • Data Lookup: Defaults to S1 stage data if lookup fails for any reason

Known Limitations

  • Currently in MVP status (v0.1) - may require refinement based on usage
  • Workflow is marked as inactive, requiring manual activation for use
  • Single LLM provider dependency (OpenRouter) without fallback options
  • Stage identification relies on pattern matching which may miss edge cases
  • No conversation history or context retention between sessions

No related workflows specified in the current context.

Setup Instructions

  1. Import Workflow: Import the JSON configuration into your n8n instance
  2. Configure Credentials:
    • Set up OpenRouter API credentials in n8n
    • Ensure the credential is named "OpenRouter account 2" or update the node reference
  3. Activate Workflow: Enable the workflow in n8n (currently set to inactive)
  4. Test Chat Interface:
    • Access the chat trigger webhook URL
    • Send test messages to verify stage identification
  5. Customize Stages: Modify the stage data in "Code:Get Stage Details" if needed for your specific pedagogical model
  6. Monitor Performance: Review AI responses and adjust prompts in "LLM:Thinking Step" as needed

The workflow will be accessible via the generated webhook URL once activated and can be integrated into chat interfaces or coaching platforms.