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

## 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
```yaml
email_automation:
inboxes:
- email: orders@company.com
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

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