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[Feature Request] Email-to-Order Automation with OCR/AI Processing

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#174 0 comentarios 1 reacción 0 asignados Ver en GitHub

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Evaluación

Dificultad
5/5
Tiempo estimado
Más de una semana
Aptitud para principiantes
20/100
Tipo de issue
Nueva funcionalidad
Claridad
Necesita aclaración
Estado de actividad
Estancado
Stack tecnológico
node.js, php
Área
ai, api, backend

Línea de trabajo

No se nombran archivos del repositorio, pruebas ni puntos de entrada. Empieza por resolver la arquitectura, los proveedores de correo electrónico, los servicios de OCR/AI, las reglas de confianza y el alcance de core-versus-plugin; el trabajo estaría terminado cuando exista un plan de implementación definido que cubra la ingesta, la extracción, la validación, la creación de pedidos, la revisión, la configuración, la monitorización y el historial de auditoría.

Escrito por el modelo de indexación a partir del texto del issue.

Descripción

Feature Request: Email-to-Order Automation with OCR/AI

Overview

Implement an intelligent email processing system that can automatically read emails from configured inboxes, extract relevant information using OCR and AI, and create orders in Fleetbase when possible.

Use Case

Many logistics and fleet management operations still receive orders via email in various formats:

  • Plain text emails with order details
  • PDF attachments with purchase orders
  • Scanned documents or images
  • Forwarded emails from customers
  • Structured or semi-structured email templates

Currently, these require manual entry into the system, which is time-consuming and error-prone.

Proposed Solution
1. Email Integration
  • IMAP/POP3 Support: Connect to any standard email inbox
  • OAuth Integration: Support for Gmail, Outlook, etc.
  • Multiple Inbox Support: Monitor different inboxes for different order types/customers
  • Email Filtering: Configure rules to process only specific emails (by sender, subject, labels)
2. Document Processing Pipeline
  • OCR Capability: Extract text from:
    • PDF attachments
    • Images (PNG, JPG, etc.)
    • Scanned documents
  • AI Text Extraction: Use LLM/NLP to understand and extract:
    • Customer information (name, contact, address)
    • Order details (items, quantities, descriptions)
    • Delivery requirements (date, time, special instructions)
    • Reference numbers (PO numbers, customer IDs)
  • Multi-language Support: Process orders in different languages
3. Order Creation Logic
  • Confidence Scoring: AI assigns confidence levels to extracted data
  • Validation Rules:
    • Required fields checking
    • Address validation
    • Customer matching against existing database
  • Draft vs Auto-Creation:
    • High confidence (>90%): Auto-create order
    • Medium confidence (60-90%): Create draft for review
    • Low confidence (<60%): Flag for manual processing
  • Duplicate Detection: Prevent duplicate orders from forwarded/resent emails
4. Configuration & Customization
  • Field Mapping: Admin can map email fields to Fleetbase order fields
  • Custom Extraction Templates: Define patterns for specific customer email formats
  • Training Interface: Allow users to correct AI extractions to improve accuracy
  • Business Rules: Set up rules for order routing, priority, assignment
5. Monitoring & Reporting
  • Processing Dashboard:
    • Emails processed
    • Orders created successfully
    • Items requiring review
    • Failed processing with reasons
  • Audit Trail: Complete history of email -> order transformation
  • Performance Metrics: Processing time, accuracy rates, error patterns
Technical Implementation Suggestions
Architecture Components:
1. Email Listener Service (Node.js worker)
   - Poll configured inboxes
   - Download attachments
   - Queue for processing

2. Document Processor
   - OCR Service (Tesseract, Google Vision API, AWS Textract)
   - AI Extraction (OpenAI API, Claude API, or open-source LLMs)
   - Data normalization

3. Order Creation Service
   - Validation engine
   - Fleetbase API integration
   - Error handling & retry logic

4. Admin Interface
   - Inbox configuration
   - Template management
   - Review queue for drafts
   - Training/correction interface
Suggested Tech Stack:
  • OCR Options:
    • Tesseract (open-source)
    • Google Cloud Vision API
    • AWS Textract
    • Azure Form Recognizer
  • AI/NLP Options:
    • OpenAI GPT-4 API
    • Anthropic Claude API
    • Open-source: Llama, Mistral
  • Email Processing:
    • Node.js IMAP/POP3 libraries
    • Bull/BullMQ for job queuing
  • Storage:
    • Original emails and attachments for audit
    • Extracted data in structured format
Benefits
  • Time Savings: Eliminate manual data entry for routine orders
  • Error Reduction: Reduce human transcription errors
  • 24/7 Processing: Orders can be created outside business hours
  • Scalability: Handle increased order volume without additional staff
  • Customer Satisfaction: Faster order processing and confirmation
Configuration Example
email_automation:
  inboxes:
    - email: [email protected]
      type: imap
      server: imap.gmail.com
      processing_rules:
        - sender_filter: "*@customer1.com"
          template: customer1_template
        - subject_contains: "Purchase Order"
          auto_create: true
          confidence_threshold: 0.85
  
  ocr_provider: google_vision
  ai_provider: openai
  
  field_mappings:
    customer_name: ["customer", "client", "ship to"]
    delivery_address: ["delivery address", "ship to address"]
    items: ["products", "items", "order details"]
Questions for Discussion
  1. Should this be a core feature or a plugin/extension?
  2. What email providers should be prioritized?
  3. Which OCR/AI services should be supported first?
  4. What level of confidence should trigger automatic order creation?
  5. How should the system handle ambiguous or incomplete information?
Related Features

This could integrate well with:

  • Existing webhooks/API integrations
  • Customer management system
  • Notification system for order confirmations
  • Reporting and analytics modules

Would love to hear thoughts from the community and maintainers on this feature request. This could significantly streamline order intake for many Fleetbase users.

Lenguaje dominante
PHP
Estrellas
36
Forks
69
Merge medio
1 d 6 h
PR fusionados (30 d)
32

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