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Create Business Summary for Youth

This workflow generates comprehensive business performance reports for youth entrepreneurs by analyzing their chat history, business data, and profile information using AI. It creates detailed summaries that help field managers quickly understand each youth's business progress, engagement level, and areas needing attention.

Purpose

No business context provided yet — add a context.md to enrich this documentation.

Based on the workflow implementation, this appears to be part of a youth entrepreneurship program that: - Monitors business performance of young entrepreneurs - Provides AI-powered coaching and support - Tracks daily sales, expenses, and profit data - Generates management reports for field staff oversight - Helps identify successful business patterns and intervention needs

How It Works

  1. Input Validation: The workflow receives a phone number and validates it exists and is not empty
  2. Data Retrieval: Fetches youth profile data from the database using the phone number
  3. Chat History Collection: Retrieves the last 30 chat messages between the youth and AI assistant
  4. Business Data Gathering: Pulls recent business tracking data including sales, costs, and micro-actions
  5. Data Aggregation: Combines chat history and business data into structured formats
  6. AI Analysis: Uses GPT-5.2 to analyze all data and generate a comprehensive business summary
  7. Summary Storage: Saves the generated summary back to the youth's profile in the database

The AI generates a detailed report including business performance trends, engagement analysis, timeline of key events, and actionable recommendations for field managers.

Workflow Diagram

graph TD
    A[When Executed by Another Workflow] --> B[If - Validate Phone Number]
    B -->|Valid| C[get_chat_history - NoOp]
    B -->|Invalid| D[No Operation, do nothing]
    C --> E[get_youth_data - PostgreSQL Query]
    E --> F[If1 - Validate Youth Data]
    F -->|Valid| G[set_yout_data - Set Variables]
    F -->|Invalid| D
    G --> H[get_chat_history1 - PostgreSQL Query]
    H --> I[aggregate_chat_history - Combine Messages]
    I --> J[get_business_data - PostgreSQL Query]
    J --> K[aggregate_business_data - Combine Business Data]
    K --> L[generate_Summary - AI Analysis]
    M[OpenRouter Chat Model] --> L
    L --> N[If2 - Validate Summary]
    N -->|Valid| O[summary - Set Summary Text]
    N -->|Invalid| D
    O --> P[add_business_summary - Update Database]

Trigger

Execute Workflow Trigger: This workflow is designed to be called by other workflows, receiving a phoneNumber parameter as input.

Nodes Used

Node Type Node Name Purpose
Execute Workflow Trigger When Executed by Another Workflow Receives phone number input from calling workflow
If If, If1, If2 Validates input data and AI output at multiple stages
No Operation get_chat_history, No Operation do nothing Placeholder and termination nodes
PostgreSQL get_youth_data, get_chat_history1, get_business_data, add_business_summary Database operations for reading youth data and storing results
Set set_yout_data, summary Data transformation and variable assignment
Aggregate aggregate_chat_history, aggregate_business_data Combines multiple database records into structured data
Chain LLM generate_Summary AI-powered analysis and report generation
OpenRouter Chat Model OpenRouter Chat Model GPT-5.2 language model for business analysis

External Services & Credentials Required

PostgreSQL Database

  • Credential: PostgresOnSupabase
  • Purpose: Stores youth profiles, chat histories, and business tracking data
  • Tables Used:
    • youthEntrepreneursReal - Youth profile and summary data
    • n8n_chat_histories - Chat conversation logs
    • dailyProfitTracking - Business performance metrics

OpenRouter API

  • Credential: OpenRouter account
  • Purpose: Access to GPT-5.2 model for AI analysis
  • Model: openai/gpt-5.2

Environment Variables

No specific environment variables are configured in this workflow. All external connections use stored credentials.

Data Flow

Input

  • phoneNumber (string): Phone number identifying the youth entrepreneur

Processing Data

  • Youth profile data (name, ward, business type, previous summary)
  • Chat history (last 30 messages with timestamps)
  • Business data (sales, costs, micro-actions, dates)
  • Current date for context

Output

  • Comprehensive business summary text stored in the youth's database profile
  • Analysis includes performance trends, engagement assessment, timeline of events, and management recommendations

Error Handling

The workflow includes multiple validation points:

  1. Input Validation: Checks if phone number exists and is not empty
  2. Data Validation: Verifies youth data was found in database
  3. Output Validation: Ensures AI generated a valid summary before saving
  4. Database Error Handling: get_youth_data node configured to continue on error with alwaysOutputData enabled

Failed validations route to a "No Operation" termination node, preventing incomplete data processing.

Known Limitations

  • Limited to last 30 chat messages and business records
  • Requires existing youth profile in database
  • AI analysis quality depends on data completeness
  • No retry mechanism for failed AI generation
  • Database update node is currently disabled in the workflow

This workflow is designed to be called by other workflows in the youth entrepreneurship program system. The trigger suggests it's part of a larger automation pipeline for monitoring and reporting.

Setup Instructions

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

  2. Configure Database Credentials:

    • Create PostgreSQL credential named "PostgresOnSupabase"
    • Ensure access to required tables: youthEntrepreneursReal, n8n_chat_histories, dailyProfitTracking

  3. Configure AI Credentials:

    • Create OpenRouter API credential named "OpenRouter account"
    • Ensure access to GPT-5.2 model

  4. Database Schema Requirements:

    • Youth table must have phoneNumber, firstName, youthWard, businessOwned, business_summary columns
    • Chat history table needs session_id, message, created_time columns
    • Business tracking table requires phoneNumber, dateOfBusiness, sales, stockingCost, transportCost, otherCost, Micro actions columns

  5. Enable Database Updates:

    • Remove the "disabled" flag from the add_business_summary node if you want summaries saved to the database

  6. Test Execution:

    • Use the pinned test data with phone number "+254794443378"
    • Verify all database connections and AI model access
    • Check that summary generation completes successfully

  7. Integration:

    • Call this workflow from other workflows using the Execute Workflow node
    • Pass the phoneNumber parameter to trigger analysis