"""Specialized strategies for autonomous business and revenue generation.""" import logging from typing import Dict, Any, List, Optional, Set, Union, Type, Tuple import json from dataclasses import dataclass, field from enum import Enum from datetime import datetime import numpy as np from collections import defaultdict from .base import ReasoningStrategy class VentureType(Enum): """Types of business ventures.""" AI_STARTUP = "ai_startup" SAAS = "saas" API_SERVICE = "api_service" DATA_ANALYTICS = "data_analytics" AUTOMATION_SERVICE = "automation_service" CONSULTING = "consulting" DIGITAL_PRODUCTS = "digital_products" MARKETPLACE = "marketplace" class RevenueStream(Enum): """Types of revenue streams.""" SUBSCRIPTION = "subscription" USAGE_BASED = "usage_based" LICENSING = "licensing" CONSULTING = "consulting" PRODUCT_SALES = "product_sales" COMMISSION = "commission" ADVERTISING = "advertising" PARTNERSHIP = "partnership" @dataclass class VentureMetrics: """Key business metrics.""" revenue: float profit_margin: float customer_acquisition_cost: float lifetime_value: float churn_rate: float growth_rate: float burn_rate: float runway_months: float roi: float @dataclass class MarketOpportunity: """Market opportunity analysis.""" market_size: float growth_potential: float competition_level: float entry_barriers: float regulatory_risks: float technology_risks: float monetization_potential: float class AIStartupStrategy(ReasoningStrategy): """ Advanced AI startup strategy that: 1. Identifies profitable AI applications 2. Analyzes market opportunities 3. Develops MVP strategies 4. Plans scaling approaches 5. Optimizes revenue streams """ def __init__(self, config: Optional[Dict[str, Any]] = None): super().__init__() self.config = config or {} # Standard reasoning parameters self.min_confidence = self.config.get('min_confidence', 0.7) self.parallel_threshold = self.config.get('parallel_threshold', 3) self.learning_rate = self.config.get('learning_rate', 0.1) self.strategy_weights = self.config.get('strategy_weights', { "LOCAL_LLM": 0.8, "CHAIN_OF_THOUGHT": 0.6, "TREE_OF_THOUGHTS": 0.5, "META_LEARNING": 0.4 }) async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]: """Generate AI startup strategy.""" try: # Market analysis market = await self._analyze_market(query, context) # Technology assessment tech = await self._assess_technology(market, context) # Business model model = await self._develop_business_model(tech, context) # Growth strategy strategy = await self._create_growth_strategy(model, context) # Financial projections projections = await self._project_financials(strategy, context) return { "success": projections["annual_profit"] >= 1_000_000, "market_analysis": market, "tech_assessment": tech, "business_model": model, "growth_strategy": strategy, "financials": projections, "confidence": self._calculate_confidence(projections) } except Exception as e: logging.error(f"Error in AI startup strategy: {str(e)}") return {"success": False, "error": str(e)} class SaaSVentureStrategy(ReasoningStrategy): """ Advanced SaaS venture strategy that: 1. Identifies scalable SaaS opportunities 2. Develops pricing strategies 3. Plans customer acquisition 4. Optimizes retention 5. Maximizes recurring revenue """ def __init__(self, config: Optional[Dict[str, Any]] = None): super().__init__() self.config = config or {} # Standard reasoning parameters self.min_confidence = self.config.get('min_confidence', 0.7) self.parallel_threshold = self.config.get('parallel_threshold', 3) self.learning_rate = self.config.get('learning_rate', 0.1) self.strategy_weights = self.config.get('strategy_weights', { "LOCAL_LLM": 0.8, "CHAIN_OF_THOUGHT": 0.6, "TREE_OF_THOUGHTS": 0.5, "META_LEARNING": 0.4 }) async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]: """Generate SaaS venture strategy.""" try: # Opportunity analysis opportunity = await self._analyze_opportunity(query, context) # Product strategy product = await self._develop_product_strategy(opportunity, context) # Pricing model pricing = await self._create_pricing_model(product, context) # Growth plan growth = await self._plan_growth(pricing, context) # Revenue projections projections = await self._project_revenue(growth, context) return { "success": projections["annual_revenue"] >= 1_000_000, "opportunity": opportunity, "product": product, "pricing": pricing, "growth": growth, "projections": projections } except Exception as e: logging.error(f"Error in SaaS venture strategy: {str(e)}") return {"success": False, "error": str(e)} class AutomationVentureStrategy(ReasoningStrategy): """ Advanced automation venture strategy that: 1. Identifies automation opportunities 2. Analyzes cost-saving potential 3. Develops automation solutions 4. Plans implementation 5. Maximizes ROI """ def __init__(self, config: Optional[Dict[str, Any]] = None): super().__init__() self.config = config or {} # Standard reasoning parameters self.min_confidence = self.config.get('min_confidence', 0.7) self.parallel_threshold = self.config.get('parallel_threshold', 3) self.learning_rate = self.config.get('learning_rate', 0.1) self.strategy_weights = self.config.get('strategy_weights', { "LOCAL_LLM": 0.8, "CHAIN_OF_THOUGHT": 0.6, "TREE_OF_THOUGHTS": 0.5, "META_LEARNING": 0.4 }) async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]: """Generate automation venture strategy.""" try: # Opportunity identification opportunities = await self._identify_opportunities(query, context) # Solution development solutions = await self._develop_solutions(opportunities, context) # Implementation strategy implementation = await self._create_implementation_strategy(solutions, context) # ROI analysis roi = await self._analyze_roi(implementation, context) # Scale strategy scale = await self._create_scale_strategy(roi, context) return { "success": roi["annual_profit"] >= 1_000_000, "opportunities": opportunities, "solutions": solutions, "implementation": implementation, "roi": roi, "scale": scale } except Exception as e: logging.error(f"Error in automation venture strategy: {str(e)}") return {"success": False, "error": str(e)} class DataVentureStrategy(ReasoningStrategy): """ Advanced data venture strategy that: 1. Identifies valuable data opportunities 2. Develops data products 3. Creates monetization strategies 4. Ensures compliance 5. Maximizes data value """ def __init__(self, config: Optional[Dict[str, Any]] = None): super().__init__() self.config = config or {} # Standard reasoning parameters self.min_confidence = self.config.get('min_confidence', 0.7) self.parallel_threshold = self.config.get('parallel_threshold', 3) self.learning_rate = self.config.get('learning_rate', 0.1) self.strategy_weights = self.config.get('strategy_weights', { "LOCAL_LLM": 0.8, "CHAIN_OF_THOUGHT": 0.6, "TREE_OF_THOUGHTS": 0.5, "META_LEARNING": 0.4 }) async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]: """Generate data venture strategy.""" try: # Data opportunity analysis opportunity = await self._analyze_data_opportunity(query, context) # Product development product = await self._develop_data_product(opportunity, context) # Monetization strategy monetization = await self._create_monetization_strategy(product, context) # Compliance plan compliance = await self._ensure_compliance(monetization, context) # Scale plan scale = await self._plan_scaling(compliance, context) return { "success": monetization["annual_revenue"] >= 1_000_000, "opportunity": opportunity, "product": product, "monetization": monetization, "compliance": compliance, "scale": scale } except Exception as e: logging.error(f"Error in data venture strategy: {str(e)}") return {"success": False, "error": str(e)} class APIVentureStrategy(ReasoningStrategy): """ Advanced API venture strategy that: 1. Identifies API opportunities 2. Develops API products 3. Creates pricing models 4. Plans scaling 5. Maximizes API value """ def __init__(self, config: Optional[Dict[str, Any]] = None): super().__init__() self.config = config or {} # Standard reasoning parameters self.min_confidence = self.config.get('min_confidence', 0.7) self.parallel_threshold = self.config.get('parallel_threshold', 3) self.learning_rate = self.config.get('learning_rate', 0.1) self.strategy_weights = self.config.get('strategy_weights', { "LOCAL_LLM": 0.8, "CHAIN_OF_THOUGHT": 0.6, "TREE_OF_THOUGHTS": 0.5, "META_LEARNING": 0.4 }) async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]: """Generate API venture strategy.""" try: # API opportunity analysis opportunity = await self._analyze_api_opportunity(query, context) # Product development product = await self._develop_api_product(opportunity, context) # Pricing strategy pricing = await self._create_api_pricing(product, context) # Scale strategy scale = await self._plan_api_scaling(pricing, context) # Revenue projections projections = await self._project_api_revenue(scale, context) return { "success": projections["annual_revenue"] >= 1_000_000, "opportunity": opportunity, "product": product, "pricing": pricing, "scale": scale, "projections": projections } except Exception as e: logging.error(f"Error in API venture strategy: {str(e)}") return {"success": False, "error": str(e)} class MarketplaceVentureStrategy(ReasoningStrategy): """ Advanced marketplace venture strategy that: 1. Identifies marketplace opportunities 2. Develops platform strategy 3. Plans liquidity generation 4. Optimizes matching 5. Maximizes transaction value """ def __init__(self, config: Optional[Dict[str, Any]] = None): super().__init__() self.config = config or {} # Standard reasoning parameters self.min_confidence = self.config.get('min_confidence', 0.7) self.parallel_threshold = self.config.get('parallel_threshold', 3) self.learning_rate = self.config.get('learning_rate', 0.1) self.strategy_weights = self.config.get('strategy_weights', { "LOCAL_LLM": 0.8, "CHAIN_OF_THOUGHT": 0.6, "TREE_OF_THOUGHTS": 0.5, "META_LEARNING": 0.4 }) async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]: """Generate marketplace venture strategy.""" try: # Opportunity analysis opportunity = await self._analyze_marketplace_opportunity(query, context) # Platform strategy platform = await self._develop_platform_strategy(opportunity, context) # Liquidity strategy liquidity = await self._create_liquidity_strategy(platform, context) # Growth strategy growth = await self._plan_marketplace_growth(liquidity, context) # Revenue projections projections = await self._project_marketplace_revenue(growth, context) return { "success": projections["annual_revenue"] >= 1_000_000, "opportunity": opportunity, "platform": platform, "liquidity": liquidity, "growth": growth, "projections": projections } except Exception as e: logging.error(f"Error in marketplace venture strategy: {str(e)}") return {"success": False, "error": str(e)} class VenturePortfolioStrategy(ReasoningStrategy): """ Advanced venture portfolio strategy that: 1. Optimizes venture mix 2. Balances risk-reward 3. Allocates resources 4. Manages dependencies 5. Maximizes portfolio value """ def __init__(self, config: Optional[Dict[str, Any]] = None): super().__init__() self.config = config or {} # Standard reasoning parameters self.min_confidence = self.config.get('min_confidence', 0.7) self.parallel_threshold = self.config.get('parallel_threshold', 3) self.learning_rate = self.config.get('learning_rate', 0.1) self.strategy_weights = self.config.get('strategy_weights', { "LOCAL_LLM": 0.8, "CHAIN_OF_THOUGHT": 0.6, "TREE_OF_THOUGHTS": 0.5, "META_LEARNING": 0.4 }) async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]: """Generate venture portfolio strategy.""" try: # Portfolio analysis analysis = await self._analyze_portfolio(query, context) # Venture selection selection = await self._select_ventures(analysis, context) # Resource allocation allocation = await self._allocate_resources(selection, context) # Risk management risk = await self._manage_risk(allocation, context) # Portfolio projections projections = await self._project_portfolio(risk, context) return { "success": projections["annual_profit"] >= 1_000_000, "analysis": analysis, "selection": selection, "allocation": allocation, "risk": risk, "projections": projections } except Exception as e: logging.error(f"Error in venture portfolio strategy: {str(e)}") return {"success": False, "error": str(e)} async def _analyze_portfolio(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]: """Analyze potential venture portfolio.""" prompt = f""" Analyze venture portfolio opportunities: Query: {query} Context: {json.dumps(context)} Consider: 1. Market opportunities 2. Technology trends 3. Resource requirements 4. Risk factors 5. Synergy potential Format as: [Analysis] Opportunities: ... Trends: ... Resources: ... Risks: ... Synergies: ... """ response = await context["groq_api"].predict(prompt) return self._parse_portfolio_analysis(response["answer"]) def _parse_portfolio_analysis(self, response: str) -> Dict[str, Any]: """Parse portfolio analysis from response.""" analysis = { "opportunities": [], "trends": [], "resources": {}, "risks": [], "synergies": [] } current_section = None for line in response.split('\n'): line = line.strip() if line.startswith('Opportunities:'): current_section = "opportunities" elif line.startswith('Trends:'): current_section = "trends" elif line.startswith('Resources:'): current_section = "resources" elif line.startswith('Risks:'): current_section = "risks" elif line.startswith('Synergies:'): current_section = "synergies" elif current_section and line: if current_section == "resources": try: key, value = line.split(':') analysis[current_section][key.strip()] = value.strip() except: pass else: analysis[current_section].append(line) return analysis def get_venture_metrics(self) -> Dict[str, Any]: """Get comprehensive venture metrics.""" return { "portfolio_metrics": { "total_ventures": len(self.ventures), "profitable_ventures": sum(1 for v in self.ventures if v.metrics.profit_margin > 0), "total_revenue": sum(v.metrics.revenue for v in self.ventures), "average_margin": np.mean([v.metrics.profit_margin for v in self.ventures]), "portfolio_roi": np.mean([v.metrics.roi for v in self.ventures]) }, "market_metrics": { "total_market_size": sum(v.opportunity.market_size for v in self.ventures), "average_growth": np.mean([v.opportunity.growth_potential for v in self.ventures]), "risk_score": np.mean([v.opportunity.regulatory_risks + v.opportunity.technology_risks for v in self.ventures]) }, "performance_metrics": { "customer_acquisition": np.mean([v.metrics.customer_acquisition_cost for v in self.ventures]), "lifetime_value": np.mean([v.metrics.lifetime_value for v in self.ventures]), "churn_rate": np.mean([v.metrics.churn_rate for v in self.ventures]), "burn_rate": sum(v.metrics.burn_rate for v in self.ventures) } } class VentureStrategy(ReasoningStrategy): """ Advanced venture strategy that combines multiple specialized strategies to generate comprehensive business plans and recommendations. """ def __init__(self, config: Optional[Dict[str, Any]] = None): """Initialize venture strategy with component strategies.""" super().__init__() self.config = config or {} # Standard reasoning parameters self.min_confidence = self.config.get('min_confidence', 0.7) self.parallel_threshold = self.config.get('parallel_threshold', 3) self.learning_rate = self.config.get('learning_rate', 0.1) self.strategy_weights = self.config.get('strategy_weights', { "LOCAL_LLM": 0.8, "CHAIN_OF_THOUGHT": 0.6, "TREE_OF_THOUGHTS": 0.5, "META_LEARNING": 0.4 }) # Initialize component strategies with shared config strategy_config = { 'min_confidence': self.min_confidence, 'parallel_threshold': self.parallel_threshold, 'learning_rate': self.learning_rate, 'strategy_weights': self.strategy_weights } self.strategies = { VentureType.AI_STARTUP: AIStartupStrategy(strategy_config), VentureType.SAAS: SaaSVentureStrategy(strategy_config), VentureType.AUTOMATION_SERVICE: AutomationVentureStrategy(strategy_config), VentureType.DATA_ANALYTICS: DataVentureStrategy(strategy_config), VentureType.API_SERVICE: APIVentureStrategy(strategy_config), VentureType.MARKETPLACE: MarketplaceVentureStrategy(strategy_config) } # Portfolio strategy for multi-venture optimization self.portfolio_strategy = VenturePortfolioStrategy(strategy_config) async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]: """ Generate venture strategy based on query and context. Args: query: The venture strategy query context: Additional context and parameters Returns: Dict containing venture strategy and confidence scores """ try: # Determine venture type from query/context venture_type = self._determine_venture_type(query, context) # Get strategy for venture type strategy = self.strategies.get(venture_type) if not strategy: raise ValueError(f"Unsupported venture type: {venture_type}") # Generate strategy strategy_result = await strategy.reason(query, context) # Get portfolio analysis portfolio_result = await self.portfolio_strategy.reason(query, context) # Combine results combined_result = self._combine_results( strategy_result, portfolio_result, venture_type ) return { 'answer': self._format_strategy(combined_result), 'confidence': combined_result.get('confidence', 0.0), 'venture_type': venture_type.value, 'strategy': strategy_result, 'portfolio_analysis': portfolio_result } except Exception as e: logging.error(f"Venture strategy generation failed: {str(e)}") return { 'error': f"Venture strategy generation failed: {str(e)}", 'confidence': 0.0 } def _determine_venture_type(self, query: str, context: Dict[str, Any]) -> VentureType: """Determine venture type from query and context.""" # Use context if available if 'venture_type' in context: return VentureType(context['venture_type']) # Simple keyword matching query_lower = query.lower() if any(term in query_lower for term in ['ai', 'ml', 'model', 'neural']): return VentureType.AI_STARTUP elif any(term in query_lower for term in ['saas', 'software', 'cloud']): return VentureType.SAAS elif any(term in query_lower for term in ['automate', 'automation', 'workflow']): return VentureType.AUTOMATION_SERVICE elif any(term in query_lower for term in ['data', 'analytics', 'insights']): return VentureType.DATA_ANALYTICS elif any(term in query_lower for term in ['api', 'service', 'endpoint']): return VentureType.API_SERVICE elif any(term in query_lower for term in ['marketplace', 'platform', 'network']): return VentureType.MARKETPLACE # Default to AI startup if unclear return VentureType.AI_STARTUP def _combine_results( self, strategy_result: Dict[str, Any], portfolio_result: Dict[str, Any], venture_type: VentureType ) -> Dict[str, Any]: """Combine strategy and portfolio results.""" return { 'venture_type': venture_type.value, 'strategy': strategy_result.get('strategy', {}), 'metrics': strategy_result.get('metrics', {}), 'portfolio_fit': portfolio_result.get('portfolio_fit', {}), 'recommendations': strategy_result.get('recommendations', []), 'confidence': min( strategy_result.get('confidence', 0.0), portfolio_result.get('confidence', 0.0) ) } def _format_strategy(self, result: Dict[str, Any]) -> str: """Format venture strategy into readable text.""" sections = [] # Venture type sections.append(f"Venture Type: {result['venture_type'].replace('_', ' ').title()}") # Strategy overview if 'strategy' in result: strategy = result['strategy'] sections.append("\nStrategy Overview:") for key, value in strategy.items(): sections.append(f"- {key.replace('_', ' ').title()}: {value}") # Key metrics if 'metrics' in result: metrics = result['metrics'] sections.append("\nKey Metrics:") for key, value in metrics.items(): if isinstance(value, (int, float)): sections.append(f"- {key.replace('_', ' ').title()}: {value:.2f}") else: sections.append(f"- {key.replace('_', ' ').title()}: {value}") # Portfolio fit if 'portfolio_fit' in result: fit = result['portfolio_fit'] sections.append("\nPortfolio Analysis:") for key, value in fit.items(): sections.append(f"- {key.replace('_', ' ').title()}: {value}") # Recommendations if 'recommendations' in result: recs = result['recommendations'] sections.append("\nKey Recommendations:") for rec in recs: sections.append(f"- {rec}") return "\n".join(sections)