League Partnership Acquisition Pipeline

# Pipeline state machine
from enum import Enum
from dataclasses import dataclass
from typing import Dict, Any
 
class PipelineStage(Enum):
    DOCUMENT_INTELLIGENCE = "document_intelligence"
    PARTNERSHIP_SCORING = "partnership_scoring"
    LEAGUE_ANALYSIS = "league_analysis"
    CONTRACT_GENERATION = "contract_generation"
    COMPLETED = "completed"
    FAILED = "failed"
 
@dataclass
class PipelineState:
    league_id: str
    current_stage: PipelineStage
    stage_data: Dict[str, Any] = None
    status: str = "active"
    created_at: datetime = None
    updated_at: datetime = None
    audit_log: List[Dict] = None
 
class PipelineOrchestrator:
    async def advance_stage(self, state: PipelineState) -> PipelineState:
        """Advance to next stage in pipeline"""
        if state.current_stage == PipelineStage.DOCUMENT_INTELLIGENCE:
            # Validate document processing
            if not await self.validate_document_intelligence(state.league_id):
                return await self.retry_stage(state)
            state.stage_data = await self.process_document_intelligence(state.league_id)
            state.current_stage = PipelineStage.PARTNERSHIP_SCORING
        
        elif state.current_stage == PipelineStage.PARTNERSHIP_SCORING:
            # Run scoring
            scoring_results = await self.run_partnership_scoring(state.stage_data)
            state.stage_data = scoring_results
            state.current_stage = PipelineStage.LEAGUE_ANALYSIS
        
        # ... other stages
        
        await self.persist_state(state)
        return state
 
    async def handle_human_review(self, state: PipelineState, approval: bool, feedback: str) -> PipelineState:
        """Handle human intervention at review points"""
        if approval:
            state.current_stage = PipelineStage.CONTRACT_GENERATION
        else:
            # Rollback or revise based on feedback
            state.current_stage = PipelineStage.LEAGUE_ANALYSIS  # Example
        state.audit_log.append({
            "stage": "human_review",
            "decision": approval,
            "feedback": feedback,
            "timestamp": datetime.utcnow()
        })
        return await self.advance_stage(state)

# Document intelligence integration
class DocumentIntelligenceIntegrator:
    async def process_league_documents(self, league_id: str, documents: List[Document]) -> DocumentIntelligenceResults:
        """Integrate with document processing MCP tools"""
        results = []
        
        for doc in documents:
            # Call MCP tool for document processing
            extraction = await self.mcp_client.call_tool(
                tool_name="process_document",
                parameters={
                    "document_id": doc.id,
                    "content": doc.content,
                    "type": doc.type
                }
            )
            
            # Validate quality
            quality_score = await self.calculate_quality_score(extraction)
            
            if quality_score < 0.7:
                # Attempt OCR fallback
                ocr_result = await self.process_with_ocr(doc)
                extraction = await self.merge_results(extraction, ocr_result)
                quality_score = await self.calculate_quality_score(extraction)
            
            # Enrich with web data
            enriched = await self.enrich_with_web_data(doc, extraction)
            
            results.append({
                "document_id": doc.id,
                "extraction": enriched,
                "quality_score": quality_score,
                "confidence": extraction.confidence
            })
        
        return {
            "league_id": league_id,
            "documents": results,
            "overall_quality": sum(r["quality_score"] for r in results) / len(results)
        }

# Partnership scoring engine
class PartnershipScoringEngine:
    DIMENSIONS = [
        "market_size",
        "technical_feasibility",
        "strategic_fit",
        "competitive_landscape",
        "growth_potential",
        "integration_complexity",
        "revenue_potential"
    ]
    
    WEIGHTS = {
        "market_size": 0.20,
        "technical_feasibility": 0.15,
        "strategic_fit": 0.15,
        "competitive_landscape": 0.15,
        "growth_potential": 0.15,
        "integration_complexity": 0.10,
        "revenue_potential": 0.10
    }
    
    async def score_partnership(self, league_data: LeagueData) -> PartnershipScore:
        """Compute comprehensive partnership score"""
        scores = {}
        reasoning = {}
        
        for dimension in self.DIMENSIONS:
            # AI-powered scoring for each dimension
            score, rationale = await self.ai_score_dimension(dimension, league_data)
            scores[dimension] = score
            reasoning[dimension] = rationale
        
        # Calculate weighted total
        total_score = sum(scores[dim] * self.WEIGHTS[dim] for dim in self.DIMENSIONS)
        
        # Determine tier
        tier = self.classify_tier(total_score)
        
        return PartnershipScore(
            dimension_scores=scores,
            total_score=total_score,
            tier=tier,
            reasoning=reasoning
        )
    
    async def ai_score_dimension(self, dimension: str, data: LeagueData) -> tuple[float, str]:
        """Use AI to score a specific dimension"""
        prompt = f"""
        Score the {dimension} of this league partnership opportunity from 0-100.
        Consider: {self.get_dimension_criteria(dimension)}
        
        League Data: {data}
        
        Provide only a number (0-100) and brief reasoning.
        """
        
        response = await self.ai_service.generate_text(prompt)
        # Parse AI response for score and reasoning
        score = self.parse_score(response)
        reasoning = self.extract_reasoning(response)
        
        return score, reasoning
    
    def classify_tier(self, score: float) -> str:
        """Classify partnership tier based on score"""
        if score > 80:
            return "Tier 1"
        elif score > 60:
            return "Tier 2"
        elif score > 40:
            return "Tier 3"
        else:
            return "Tier 4"

// Neo4j schema and sample queries
// Create league node
CREATE (l:League {
  id: $league_id,
  name: $league_name,
  sport: $sport,
  tier: $tier,
  location: $location,
  founded: $founded_year,
  member_count: $member_count
})
 
// Create sport node and relationship
MERGE (s:Sport {name: $sport})
CREATE (l)-[:OPERATES_IN]->(s)
 
// Create region node and relationship
MERGE (r:Region {name: $region})
CREATE (l)-[:LOCATED_IN]->(r)
 
// Query similar leagues for benchmarking
MATCH (l:League {id: $league_id})-[:OPERATES_IN]->(s:Sport)
MATCH (similar:League)-[:OPERATES_IN]->(s)
WHERE similar.tier = l.tier
  AND similar.member_count > l.member_count * 0.8
  AND similar.member_count < l.member_count * 1.2
RETURN similar
ORDER BY similar.member_count DESC
LIMIT 10
 
// Find partnership opportunities
MATCH (l:League {id: $league_id})-[:OPERATES_IN]->(s:Sport)
MATCH (p:Partner)-[:INTERESTED_IN]->(s)
WHERE NOT (l)-[:HAS_PARTNERSHIP_WITH]-(p)
RETURN p, s
ORDER BY p.budget DESC
LIMIT 5

# Market intelligence analysis
class MarketIntelligenceAnalyzer:
    async def analyze_market_context(self, league_id: str) -> MarketIntelligenceReport:
        """Perform comprehensive market intelligence analysis"""
        
        # Query Neo4j for competitive landscape
        competitors = await self.neo4j_client.execute_query("""
            MATCH (l:League {id: $league_id})-[:OPERATES_IN]->(s:Sport)
            MATCH (comp:League)-[:OPERATES_IN]->(s)
            WHERE comp <> l AND comp.tier = l.tier
            RETURN comp.name, comp.member_count, comp.revenue_estimate
            ORDER BY comp.member_count DESC
            LIMIT 5
        """, {"league_id": league_id})
        
        # Analyze regional saturation
        regional_data = await self.analyze_regional_saturation(league_id)
        
        # Identify similar partnership patterns
        patterns = await self.identify_successful_patterns(league_id)
        
        # Generate betting market analysis
        betting_analysis = await self.analyze_betting_coverage(league_id)
        
        return MarketIntelligenceReport(
            competitors=competitors,
            regional_saturation=regional_data,
            successful_patterns=patterns,
            betting_market=betting_analysis,
            recommendations=await self.generate_recommendations(league_id)
        )

# Tier recommendation engine
class TierRecommendationEngine:
    TIER_THRESHOLDS = {
        "Tier 1": (80, 100),
        "Tier 2": (60, 80),
        "Tier 3": (40, 60),
        "Tier 4": (0, 40)
    }
    
    PRICING_TIERS = {
        "Tier 1": {"base_price": 5000, "sla": "99.9%", "delivery": "real-time"},
        "Tier 2": {"base_price": 2500, "sla": "99.5%", "delivery": "5-min"},
        "Tier 3": {"base_price": 1000, "sla": "99.0%", "delivery": "15-min"},
        "Tier 4": {"base_price": 500, "sla": "95.0%", "delivery": "hourly"}
    }
    
    def recommend_tier(self, score: float, market_size: float, strategic_fit: float) -> TierRecommendation:
        """Recommend contract tier based on multiple factors"""
        adjusted_score = self.adjust_score_for_market(score, market_size, strategic_fit)
        
        for tier_name, (min_score, max_score) in self.TIER_THRESHOLDS.items():
            if min_score <= adjusted_score <= max_score:
                tier_config = self.PRICING_TIERS[tier_name]
                return TierRecommendation(
                    tier=f"{tier_name} ({adjusted_score:.1f})",
                    base_price=tier_config["base_price"],
                    sla=tier_config["sla"],
                    delivery_frequency=tier_config["delivery"],
                    market_adjustment=self.calculate_market_adjustment(market_size),
                    strategic_multiplier=strategic_fit / 100
                )
        
        return TierRecommendation(
            tier="Tier 4 (Emerging)",
            base_price=500,
            sla="95.0%",
            delivery_frequency="hourly",
            market_adjustment=1.0,
            strategic_multiplier=1.0
        )

# Contract generation service
class ContractGenerationService:
    async def generate_contract_package(self, league_id: str, tier: str, terms: ContractTerms) -> ContractPackage:
        """Generate complete contract package for approved tier"""
        
        # Load tier-specific templates
        templates = await self.template_library.load_templates(tier)
        
        # Render MSA
        msa_content = await self.render_template(
            template=templates.msa,
            context={
                "league_name": await self.get_league_name(league_id),
                "tier": tier,
                "pricing": terms.pricing,
                "sla": terms.sla,
                "data_delivery": terms.data_delivery
            }
        )
        
        # Render term sheet
        term_sheet = await self.render_template(
            template=templates.term_sheet,
            context=terms.to_dict()
        )
        
        # Generate technical specifications
        tech_specs = await self.generate_technical_specifications(league_id, tier)
        
        # Create PDF package
        pdf_package = await self.pdf_generator.create_package(
            msa=msa_content,
            term_sheet=term_sheet,
            tech_specs=tech_specs,
            league_id=league_id
        )
        
        return ContractPackage(
            msa=msa_content,
            term_sheet=term_sheet,
            technical_specifications=tech_specs,
            pdf=pdf_package,
            signature_required=True
        )

# Pipeline analytics service
class PipelineAnalyticsService:
    async def generate_pipeline_dashboard(self, time_period: str = "30d") -> PipelineDashboard:
        """Generate comprehensive pipeline analytics dashboard"""
        
        # Stage conversion metrics
        conversion_rates = await self.calculate_stage_conversions(time_period)
        
        # Processing time analysis
        processing_times = await self.analyze_processing_times(time_period)
        
        # Scoring distribution
        score_distribution = await self.analyze_scoring_distribution(time_period)
        
        # Tier mix analysis
        tier_mix = await self.calculate_tier_mix(time_period)
        
        # Revenue projections
        revenue_projections = await self.project_pipeline_revenue(time_period)
        
        return PipelineDashboard(
            conversion_rates=conversion_rates,
            processing_times=processing_times,
            score_distribution=score_distribution,
            tier_mix=tier_mix,
            revenue_projections=revenue_projections,
            pipeline_health=await self.assess_pipeline_health()
        )

# Human-in-the-loop approval system
class HumanApprovalWorkflow:
    async def request_approval(self, decision_point: str, context: Dict, approvers: List[str]) -> ApprovalResult:
        """Request human approval for critical decisions"""
        
        # Create approval request
        approval_request = ApprovalRequest(
            id=str(uuid4()),
            decision_point=decision_point,
            context=context,
            approvers=approvers,
            status="pending",
            created_at=datetime.utcnow(),
            timeout_at=datetime.utcnow() + timedelta(hours=4)
        )
        
        # Store in database
        await self.approval_repository.create(approval_request)
        
        # Notify approvers
        await self.notification_service.send_approval_request(
            approvers,
            approval_request,
            context.get("league_name", "Unknown League")
        )
        
        # Set up timeout handler
        asyncio.create_task(self.handle_timeout(approval_request))
        
        return ApprovalResult(
            request_id=approval_request.id,
            status="pending",
            timeout_remaining=4 * 60 * 60  # 4 hours in seconds
        )
    
    async def handle_approval_response(self, request_id: str, approved: bool, rationale: str, approver_id: str) -> ApprovalResult:
        """Process approval response from human reviewer"""
        
        request = await self.approval_repository.get_by_id(request_id)
        if not request:
            raise ValueError("Approval request not found")
        
        # Update request
        request.status = "approved" if approved else "rejected"
        request.decision_rationale = rationale
        request.approver_id = approver_id
        request.completed_at = datetime.utcnow()
        
        await self.approval_repository.update(request)
        
        # Log audit trail
        await self.audit_log.record_human_decision(
            request_id=request_id,
            decision=approved,
            rationale=rationale,
            approver_id=approver_id
        )
        
        # Notify pipeline orchestrator
        await self.pipeline_orchestrator.handle_human_decision(
            request.decision_point,
            approved,
            rationale
        )
        
        return ApprovalResult(
            request_id=request_id,
            status=request.status,
            decision=approved,
            rationale=rationale
        )

# CRM integration service
class CRMIntegrationService:
    async def sync_pipeline_to_jira(self, pipeline_entry: PipelineEntry) -> JiraIssue:
        """Create/update Jira issue for pipeline entry"""
        
        issue_data = {
            "project": {"key": "LPP"},
            "summary": f"Partnership Pipeline: {pipeline_entry.league_name}",
            "description": self.format_jira_description(pipeline_entry),
            "issuetype": {"name": "Task"},
            "customfield_10010": pipeline_entry.tier,  # Custom field for tier
            "customfield_10011": pipeline_entry.total_score  # Custom field for score
        }
        
        issue = await self.jira_client.create_issue(issue_data)
        
        # Link pipeline entry to Jira issue
        await self.update_pipeline_crm_link(pipeline_entry.id, issue.key)
        
        return issue
    
    async def update_jira_status(self, pipeline_entry_id: str, new_stage: str) -> None:
        """Update Jira issue status based on pipeline stage"""
        
        pipeline_entry = await self.get_pipeline_entry(pipeline_entry_id)
        jira_issue_key = pipeline_entry.jira_issue_key
        
        status_map = {
            "document_intelligence": "In Progress",
            "partnership_scoring": "In Review",
            "league_analysis": "Analysis Complete",
            "contract_generation": "Ready for Contract",
            "completed": "Done",
            "failed": "Blocked"
        }
        
        status = status_map.get(pipeline_entry.current_stage.value, "To Do")
        
        await self.jira_client.update_issue(
            issue_key=jira_issue_key,
            transition={"id": await self.get_status_id(status)}
        )
        
        # Add comment with stage details
        await self.jira_client.add_comment(
            issue_key=jira_issue_key,
            body=f"Pipeline advanced to {new_stage}. Score: {pipeline_entry.total_score}"
        )

# Pipeline state management
@dataclass
class PipelineCheckpoint:
    stage: PipelineStage
    data: Dict[str, Any]
    timestamp: datetime
    status: str
    checksum: str  # For integrity validation
 
class PipelineStateManager:
    async def save_checkpoint(self, league_id: str, stage: PipelineStage, data: Dict) -> str:
        """Save checkpoint for pipeline stage"""
        
        checkpoint = PipelineCheckpoint(
            stage=stage,
            data=data,
            timestamp=datetime.utcnow(),
            status="completed",
            checksum=self.calculate_checksum(data)
        )
        
        # Store in Firestore with transaction
        doc_ref = self.firestore.collection("pipelines").document(league_id).collection("checkpoints").document(stage.value)
        await self.firestore.run_transaction(
            lambda transaction: transaction.set(doc_ref, checkpoint.__dict__)
        )
        
        return checkpoint.checksum
    
    async def restore_checkpoint(self, league_id: str, stage: PipelineStage) -> Optional[PipelineCheckpoint]:
        """Restore pipeline state from checkpoint"""
        
        doc_ref = self.firestore.collection("pipelines").document(league_id).collection("checkpoints").document(stage.value)
        doc = await doc_ref.get()
        
        if doc.exists:
            checkpoint_data = doc.to_dict()
            checkpoint = PipelineCheckpoint(**checkpoint_data)
            
            # Validate integrity
            if checkpoint.checksum != self.calculate_checksum(checkpoint.data):
                raise ValueError("Checkpoint integrity validation failed")
            
            return checkpoint
        
        return None
    
    async def replay_stage(self, league_id: str, stage: PipelineStage) -> PipelineCheckpoint:
        """Replay a specific stage from previous checkpoint"""
        
        previous_checkpoint = await self.restore_checkpoint(league_id, stage)
        if not previous_checkpoint:
            raise ValueError(f"No checkpoint found for stage {stage}")
        
        # Re-execute stage with same inputs
        new_data = await self.execute_stage(stage, previous_checkpoint.data)
        
        # Save new checkpoint
        new_checksum = await self.save_checkpoint(league_id, stage, new_data)
        
        return PipelineCheckpoint(
            stage=stage,
            data=new_data,
            timestamp=datetime.utcnow(),
            status="replayed",
            checksum=new_checksum
        )

# Performance monitoring
class PipelinePerformanceMonitor:
    SLAs = {
        "document_intelligence": 30,  # seconds
        "partnership_scoring": 60,
        "league_analysis": 90,
        "contract_generation": 120
    }
    
    async def monitor_stage_performance(self, league_id: str, stage: PipelineStage, start_time: datetime) -> PerformanceMetrics:
        """Monitor and record stage performance"""
        
        end_time = datetime.utcnow()
        duration = (end_time - start_time).total_seconds()
        sla_target = self.SLAs[stage.value]
        is_within_sla = duration <= sla_target
        
        metrics = PerformanceMetrics(
            league_id=league_id,
            stage=stage.value,
            duration=duration,
            sla_target=sla_target,
            within_sla=is_within_sla,
            timestamp=end_time
        )
        
        # Record metrics
        await self.metrics_service.record_pipeline_metrics(metrics)
        
        # Alert if SLA violation
        if not is_within_sla:
            await self.alerting_service.send_sla_violation_alert(
                league_id=league_id,
                stage=stage.value,
                expected=sla_target,
                actual=duration
            )
        
        return metrics