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AI Brain MVP

An intelligent coaching assistant that analyzes youth entrepreneur situations and provides stage-specific guidance to coaches working with young entrepreneurs in East Africa. The workflow uses AI to identify which stage of a 15-stage pedagogical model a youth is at, then suggests practical next steps for coaches to help their youth progress.

Purpose

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This workflow serves as an AI-powered coaching tool that helps peer leaders and mobilizers support youth entrepreneurs more effectively. When coaches describe a situation with a youth they're supporting, the AI analyzes the youth's current entrepreneurial stage and provides specific, actionable advice for what the coach should say or do next.

How It Works

  1. Receive Coach Input: A coach describes their youth's situation through a chat interface
  2. Load Stage Framework: The system loads the 15-stage pedagogical model that guides youth from business startup to sustainable growth
  3. Analyze Youth Stage: AI analyzes the coach's description to determine which stage (S1-S15) the youth is currently at
  4. Extract Stage Information: The system identifies the specific stage and retrieves detailed information about it
  5. Get Stage Details: Comprehensive stage data is loaded, including goals, skills, micro-actions, and common bottlenecks
  6. Generate Coaching Suggestions: AI creates specific, practical advice for what the coach should say or do next with their youth
  7. Format Response: The final coaching suggestions are formatted and returned to the coach

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]
    E --> F[LLM:Suggest Next Steps]
    F --> G[Code:Format Output]

    H[OpenRouter:Thinking] -.-> C
    I[OpenRouter:Suggestions] -.-> F

Trigger

Chat Trigger: The workflow starts when a chat message is received through a public webhook. Coaches can interact with the AI assistant by describing their youth's situation in natural language.

Nodes Used

Node Type Node Name Purpose
Chat Trigger When chat message received Receives coach input through chat interface
Set Set:Load Stage Data Loads the 15-stage pedagogical framework and user input
LLM Chain LLM:Thinking Step Analyzes youth situation to determine current stage
Code Code:Extract Stage ID Parses AI response to extract specific stage identifier
Code Code:Get Stage Details Retrieves comprehensive data for identified stage
LLM Chain LLM:Suggest Next Steps Generates coaching advice based on stage analysis
Code Code:Format Output Formats final response for coach
OpenRouter LLM OpenRouter:Thinking AI model for stage analysis
OpenRouter LLM OpenRouter:Suggestions AI model for generating coaching suggestions

External Services & Credentials Required

  • OpenRouter API: Required for AI language model access
    • Credential: openRouterApi (OpenRouter account 2)
    • Used for both stage analysis and suggestion generation

Environment Variables

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

Data Flow

Input: - Coach's description of youth situation (via chat) - Session ID for conversation tracking

Processing: - 15-stage pedagogical model framework - Stage-specific goals, skills, micro-actions, and bottlenecks - AI analysis of youth's current developmental stage

Output: - Specific coaching suggestions for what the coach should say or do next - Stage-appropriate micro-actions for the youth - Guidance on addressing common bottlenecks

Error Handling

The workflow includes basic error handling: - If no valid stage is identified from AI analysis, defaults to Stage 1 (S1) - Fallback logic ensures a stage is always assigned even if pattern matching fails - Multiple AI response formats are supported (text or response fields)

Known Limitations

  • Relies on coach's description quality for accurate stage identification
  • Limited to 15 predefined stages in the pedagogical model
  • Requires manual translation if coach needs guidance in local languages
  • No conversation memory between sessions

No related workflows specified in the current context.

Setup Instructions

  1. Import Workflow: Import the JSON workflow into your n8n instance

  2. Configure OpenRouter Credentials:

    • Create an OpenRouter API account
    • Add OpenRouter API credentials in n8n with ID: Az2rKAl4uVoFJhBr
    • Name the credential "OpenRouter account 2"

  3. Activate Workflow:

    • Ensure the workflow is set to "Active"
    • The chat trigger will generate a public webhook URL

  4. Test the Setup:

    • Send a test message describing a youth entrepreneur situation
    • Verify the AI responds with stage analysis and coaching suggestions

  5. Share with Coaches:

    • Provide coaches with the chat interface URL
    • Train them on how to describe youth situations effectively
    • Explain how to interpret and use the AI's coaching suggestions

The workflow is ready to use once credentials are configured and the workflow is activated.