⚙️ AI Automation

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┌─────────────────────────────────────────────────────────────┐
│                    Input Sources                             │
│       (Email, Documents, Forms, APIs, Webhooks)             │
└─────────────────────────────────────────────────────────────┘
                              │
                              ▼
┌─────────────────────────────────────────────────────────────┐
│                  AI Classification                           │
│        (Document type, intent, urgency, routing)            │
└─────────────────────────────────────────────────────────────┘
                              │
                              ▼
┌─────────────────────────────────────────────────────────────┐
│                  AI Extraction & Analysis                    │
│    (Entity extraction, summarization, decision support)     │
└─────────────────────────────────────────────────────────────┘
                              │
        ┌─────────────────────┼─────────────────────┐
        │                     │                     │
        ▼                     ▼                     ▼
┌───────────────┐   ┌─────────────────┐   ┌───────────────┐
│  Auto-Process │   │  Human Review   │   │   Exception   │
│  (High Conf)  │   │  (Low Conf)     │   │   Handling    │
└───────────────┘   └─────────────────┘   └───────────────┘
        │                     │                     │
        └─────────────────────┼─────────────────────┘
                              │
                              ▼
┌─────────────────────────────────────────────────────────────┐
│                    Action Execution                          │
│         (Update systems, send responses, file docs)         │
└─────────────────────────────────────────────────────────────┘

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from langchain.chains import LLMChain
from pydantic import BaseModel

class DocumentProcessor:
    def __init__(self):
        self.classifier = DocumentClassifier()
        self.extractor = DataExtractor()
        self.router = WorkflowRouter()
    
    async def process(self, document: bytes) -> ProcessResult:
        # Step 1: Classify document
        doc_type = await self.classifier.classify(document)
        
        # Step 2: Extract data based on type
        schema = self.get_schema(doc_type)
        extracted = await self.extractor.extract(document, schema)
        
        # Step 3: Validate extraction
        validation = self.validate(extracted)
        
        # Step 4: Route based on confidence
        if validation.confidence > 0.95:
            return await self.auto_process(extracted)
        elif validation.confidence > 0.7:
            return await self.queue_for_review(extracted)
        else:
            return await self.escalate(document, validation.issues)
    
    async def auto_process(self, data: ExtractedData):
        # Execute automated actions
        await self.update_crm(data)
        await self.file_document(data)
        await self.notify_stakeholders(data)
        return ProcessResult(status="completed", automated=True)

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class EmailAutomation:
    def __init__(self):
        self.llm = get_llm()
        self.style_matcher = StyleMatcher()
    
    async def generate_response(
        self, 
        email: Email, 
        user: User
    ) -> DraftResponse:
        # Understand the email
        analysis = await self.analyze_email(email)
        
        # Match user's writing style
        style = await self.style_matcher.get_style(user)
        
        # Generate contextual response
        prompt = f"""
        Email to respond to: {email.body}
        User's typical style: {style.description}
        Tone: {style.tone}
        
        Generate a professional response.
        """
        
        response = await self.llm.generate(prompt)
        
        return DraftResponse(
            content=response,
            confidence=analysis.confidence,
            suggestions=analysis.suggestions
        )