Scaling AI Reddit Automation: From Single Account to Enterprise-Grade Systems

class AccountPoolManager:
    def __init__(self):
        self.accounts = {
            'technical_expert': [
                {'username': 'dev_insights_pro', 'specialization': ['webdev', 'programming']},
                {'username': 'tech_solutions_guide', 'specialization': ['sysadmin', 'devops']},
            ],
            'business_advisor': [
                {'username': 'startup_strategist', 'specialization': ['entrepreneur', 'smallbusiness']},
                {'username': 'growth_consultant', 'specialization': ['marketing', 'sales']},
            ],
            'industry_specialist': [
                {'username': 'saas_insights', 'specialization': ['saas', 'b2b']},
                {'username': 'ecommerce_expert', 'specialization': ['ecommerce', 'retail']},
            ]
        }

    def select_optimal_account(self, subreddit: str, post_topic: str, conversation_history: dict):
        """Choose the most appropriate account for this interaction"""

        # Match account expertise to context
        for account_type, accounts in self.accounts.items():
            for account in accounts:
                if subreddit in account['specialization']:
                    # Check account health and availability
                    if self.is_account_healthy(account['username'], subreddit):
                        return account['username']

        # Fallback to least active account
        return self.get_least_active_account(subreddit)

    def is_account_healthy(self, username: str, subreddit: str) -> bool:
        """Check if account is in good standing for this subreddit"""
        return (
            self.get_daily_posts(username, subreddit) < 3 and  # Not over-posting
            self.get_recent_upvote_ratio(username) > 0.7 and   # Well received
            not self.is_recently_warned(username, subreddit)    # No mod warnings
        )

class GeographicScaler:
    def __init__(self):
        self.regional_accounts = {
            'US_East': {
                'active_hours': (6, 22),  # EST
                'cultural_tone': 'direct, results-focused',
                'accounts': ['us_business_advisor', 'east_coast_tech'],
            },
            'US_West': {
                'active_hours': (6, 22),  # PST
                'cultural_tone': 'innovative, startup-friendly',
                'accounts': ['west_coast_growth', 'silicon_valley_insights'],
            },
            'UK': {
                'active_hours': (7, 23),  # GMT
                'cultural_tone': 'polite, understated',
                'accounts': ['uk_business_guide', 'london_tech_advisor'],
            },
            'AU': {
                'active_hours': (6, 22),  # AEST
                'cultural_tone': 'casual, straightforward',
                'accounts': ['aussie_business_mate', 'sydney_startup_help'],
            }
        }

    def get_active_regions(self, current_time_utc):
        """Determine which regions should be active now"""
        active_regions = []

        for region, config in self.regional_accounts.items():
            local_time = self.convert_to_local_time(current_time_utc, region)
            if config['active_hours'][0] <= local_time.hour <= config['active_hours'][1]:
                active_regions.append(region)

        return active_regions

    def adapt_response_for_region(self, base_response: str, region: str) -> str:
        """Adapt response tone and content for regional preferences"""
        cultural_tone = self.regional_accounts[region]['cultural_tone']

        if 'polite, understated' in cultural_tone:
            # UK adaptation: softer language, more modest claims
            return self.make_more_understated(base_response)
        elif 'casual, straightforward' in cultural_tone:
            # AU adaptation: more casual language
            return self.make_more_casual(base_response)

        return base_response

class ModelOrchestrator:
    def __init__(self):
        self.model_assignments = {
            'planner': {
                'model': 'claude-3-sonnet',
                'use_case': 'strategic thinking, complex reasoning',
                'cost_per_request': 0.015
            },
            'navigator': {
                'model': 'gpt-4o-mini',
                'use_case': 'fast execution, simple decisions',
                'cost_per_request': 0.0015
            },
            'quality_controller': {
                'model': 'gpt-4o',
                'use_case': 'quality assessment, nuanced judgment',
                'cost_per_request': 0.01
            }
        }

    def optimize_model_selection(self, task_complexity: str, volume_requirements: int):
        """Choose optimal model based on complexity and volume"""

        if task_complexity == 'high' and volume_requirements < 1000:
            # High quality, moderate volume
            return 'claude-3-sonnet'
        elif task_complexity == 'medium' and volume_requirements > 5000:
            # Balanced quality and speed for high volume
            return 'gpt-4o-mini'
        elif task_complexity == 'high' and volume_requirements > 5000:
            # Need quality but at scale - use tiered approach
            return self.implement_tiered_processing()

    def implement_tiered_processing(self):
        """Use cheap models for initial filtering, expensive for final decisions"""
        return {
            'tier_1_filter': 'gpt-4o-mini',     # Fast, cheap initial assessment
            'tier_2_quality': 'claude-3-sonnet', # High-quality final processing
            'routing_threshold': 0.7              # Confidence threshold for tier 2
        }

class ResponseOptimizer:
    def __init__(self):
        self.response_cache = ResponseCache()
        self.template_library = TemplateLibrary()

    def generate_optimized_response(self, post_context: dict, account_profile: dict):
        """Generate response using caching and optimization"""

        # Check for similar previous responses
        cached_response = self.response_cache.find_similar(
            post_context,
            similarity_threshold=0.85
        )

        if cached_response and cached_response['performance_score'] > 0.8:
            # Adapt cached response for current context
            return self.adapt_cached_response(cached_response, post_context)

        # Check if this matches a proven template pattern
        template_match = self.template_library.find_pattern_match(post_context)

        if template_match:
            # Use template with personalization
            return self.personalize_template(template_match, post_context, account_profile)

        # Generate fresh response for novel situations
        return self.generate_fresh_response(post_context, account_profile)

    def adapt_cached_response(self, cached_response: dict, current_context: dict):
        """Modify cached response for current situation"""
        base_response = cached_response['text']

        # Update specific details
        adapted_response = self.update_context_specific_details(base_response, current_context)

        # Vary language to avoid detection
        adapted_response = self.add_natural_variation(adapted_response)

        return adapted_response

class ScalableQualityControl:
    def __init__(self):
        self.quality_layers = [
            BasicContentFilter(),
            CommunityAppropriatenessChecker(),
            BrandConsistencyValidator(),
            PerformancePredictionModel()
        ]

    def evaluate_response_quality(self, response: dict, context: dict) -> dict:
        """Run response through multiple quality layers"""

        quality_scores = {}
        blocking_issues = []

        for layer in self.quality_layers:
            layer_result = layer.evaluate(response, context)
            quality_scores[layer.name] = layer_result['score']

            if layer_result['blocking_issues']:
                blocking_issues.extend(layer_result['blocking_issues'])

        # Calculate composite quality score
        composite_score = self.calculate_composite_score(quality_scores)

        # Predict performance based on historical data
        predicted_performance = self.predict_community_reception(response, context)

        return {
            'composite_quality_score': composite_score,
            'predicted_upvotes': predicted_performance['upvotes'],
            'predicted_engagement': predicted_performance['engagement'],
            'blocking_issues': blocking_issues,
            'approved': composite_score > 0.75 and len(blocking_issues) == 0
        }

    def predict_community_reception(self, response: dict, context: dict) -> dict:
        """Use ML model trained on historical response performance"""
        features = self.extract_performance_features(response, context)

        # Features might include: response length, sentiment, timing, subreddit history, etc.
        predicted_score = self.performance_model.predict(features)

        return {
            'upvotes': predicted_score['upvotes'],
            'engagement': predicted_score['replies'],
            'confidence': predicted_score['confidence']
        }

class ResponseABTester:
    def __init__(self):
        self.active_experiments = {}
        self.statistical_engine = StatisticalTestingEngine()

    def create_response_experiment(self, base_response: str, variations: list, context: dict):
        """Set up A/B test for response variations"""

        experiment = {
            'id': self.generate_experiment_id(),
            'base_response': base_response,
            'variations': variations,
            'context': context,
            'results': {variant: [] for variant in ['control'] + variations},
            'traffic_split': 1.0 / (len(variations) + 1),
            'start_time': datetime.now(),
            'target_sample_size': self.calculate_sample_size(context)
        }

        self.active_experiments[experiment['id']] = experiment
        return experiment['id']

    def assign_response_variant(self, experiment_id: str, post_id: str) -> str:
        """Assign this post to a specific response variant"""
        experiment = self.active_experiments[experiment_id]

        # Use post_id hash for consistent assignment
        variant_index = hash(post_id) % len(experiment['variations'] + ['control'])

        if variant_index == 0:
            assigned_variant = 'control'
            response = experiment['base_response']
        else:
            assigned_variant = experiment['variations'][variant_index - 1]
            response = assigned_variant

        # Log assignment for analysis
        self.log_assignment(experiment_id, post_id, assigned_variant)

        return response

    def analyze_experiment_results(self, experiment_id: str) -> dict:
        """Analyze A/B test results and determine winner"""
        experiment = self.active_experiments[experiment_id]

        # Gather performance data for each variant
        variant_performance = {}
        for variant in ['control'] + experiment['variations']:
            performance_data = self.get_variant_performance(experiment_id, variant)
            variant_performance[variant] = performance_data

        # Statistical significance testing
        significance_results = self.statistical_engine.analyze_significance(variant_performance)

        # Determine winning variant
        winner = self.statistical_engine.select_winner(
            variant_performance,
            significance_results,
            minimum_confidence=0.95
        )

        return {
            'winner': winner,
            'confidence': significance_results[winner]['confidence'],
            'improvement': significance_results[winner]['improvement_over_control'],
            'recommendation': self.generate_recommendation(winner, significance_results)
        }

class CrossPlatformOrchestrator:
    def __init__(self):
        self.platforms = {
            'reddit': RedditAutomation(),
            'twitter': TwitterAutomation(),
            'linkedin': LinkedInAutomation(),
            'discord': DiscordAutomation()
        }

    def orchestrate_multi_platform_campaign(self, content_seed: str, target_audiences: dict):
        """Coordinate content across multiple platforms"""

        # Generate platform-specific content from seed
        platform_content = {}
        for platform, audience in target_audiences.items():
            adapted_content = self.adapt_content_for_platform(
                content_seed,
                platform,
                audience
            )
            platform_content[platform] = adapted_content

        # Coordinate timing for maximum impact
        posting_schedule = self.optimize_cross_platform_timing(platform_content)

        # Execute coordinated posting
        results = {}
        for platform, timing in posting_schedule.items():
            results[platform] = self.schedule_platform_post(
                platform,
                platform_content[platform],
                timing
            )

        return results

    def adapt_content_for_platform(self, content: str, platform: str, audience: dict) -> str:
        """Adapt content for platform-specific audience and format"""

        adaptations = {
            'reddit': {
                'style': 'conversational, helpful, community-focused',
                'length': 'medium to long-form',
                'tone': 'authentic, value-first'
            },
            'twitter': {
                'style': 'concise, engaging, thread-capable',
                'length': 'short-form with thread option',
                'tone': 'professional but accessible'
            },
            'linkedin': {
                'style': 'professional, insight-driven, network-building',
                'length': 'medium-form with professional depth',
                'tone': 'authoritative but approachable'
            }
        }

        platform_requirements = adaptations[platform]

        # Use AI to adapt content maintaining core message
        adapted_content = self.ai_adapter.adapt_content(
            original_content=content,
            target_style=platform_requirements['style'],
            target_length=platform_requirements['length'],
            target_tone=platform_requirements['tone'],
            audience_profile=audience
        )

        return adapted_content

class EnterpriseAnalytics:
    def __init__(self):
        self.metrics_collector = MetricsCollector()
        self.performance_analyzer = PerformanceAnalyzer()

    def generate_comprehensive_analytics(self, time_range: dict) -> dict:
        """Generate enterprise-level performance analytics"""

        return {
            'engagement_metrics': self.analyze_engagement_performance(time_range),
            'conversion_tracking': self.analyze_conversion_funnel(time_range),
            'account_health': self.assess_account_portfolio_health(time_range),
            'competitive_positioning': self.analyze_competitive_landscape(time_range),
            'roi_analysis': self.calculate_comprehensive_roi(time_range),
            'optimization_recommendations': self.generate_optimization_insights(time_range)
        }

    def analyze_engagement_performance(self, time_range: dict) -> dict:
        """Deep dive into engagement quality and patterns"""

        metrics = self.metrics_collector.get_engagement_data(time_range)

        return {
            'response_rates_by_subreddit': self.calculate_response_rates(metrics),
            'upvote_patterns_by_account': self.analyze_upvote_trends(metrics),
            'conversation_depth_analysis': self.measure_conversation_quality(metrics),
            'timing_optimization_insights': self.analyze_optimal_posting_times(metrics),
            'content_performance_by_strategy': self.compare_strategy_effectiveness(metrics)
        }

    def generate_optimization_insights(self, time_range: dict) -> list:
        """AI-powered recommendations for improving performance"""

        current_performance = self.performance_analyzer.get_current_metrics(time_range)
        historical_trends = self.performance_analyzer.get_historical_trends()

        insights = []

        # Identify underperforming areas
        underperforming_accounts = self.identify_underperforming_accounts(current_performance)
        if underperforming_accounts:
            insights.append({
                'type': 'account_optimization',
                'priority': 'high',
                'accounts': underperforming_accounts,
                'recommended_actions': self.generate_account_improvement_plan(underperforming_accounts)
            })

        # Identify high-opportunity subreddits
        opportunity_subreddits = self.identify_expansion_opportunities(current_performance)
        if opportunity_subreddits:
            insights.append({
                'type': 'expansion_opportunity',
                'priority': 'medium',
                'subreddits': opportunity_subreddits,
                'potential_impact': self.estimate_expansion_impact(opportunity_subreddits)
            })

        return insights

class CostOptimizer:
    def __init__(self):
        self.cost_models = {
            'openai_gpt4': {'input': 0.03, 'output': 0.06},
            'openai_gpt4_mini': {'input': 0.00015, 'output': 0.0006},
            'claude_sonnet': {'input': 0.003, 'output': 0.015},
            'claude_haiku': {'input': 0.00025, 'output': 0.00125},
            'local_llama': {'input': 0, 'output': 0, 'infrastructure': 200}  # Monthly server cost
        }

    def optimize_model_allocation(self, volume_projections: dict, quality_requirements: dict):
        """Optimize AI model selection across different use cases"""

        optimization_plan = {}

        for use_case, volume in volume_projections.items():
            quality_threshold = quality_requirements.get(use_case, 0.7)

            # Calculate cost-effectiveness for each model
            model_options = []
            for model, pricing in self.cost_models.items():
                estimated_quality = self.estimate_model_quality(model, use_case)
                estimated_cost = self.calculate_monthly_cost(model, volume)

                if estimated_quality >= quality_threshold:
                    cost_effectiveness = estimated_quality / estimated_cost
                    model_options.append({
                        'model': model,
                        'quality': estimated_quality,
                        'cost': estimated_cost,
                        'effectiveness': cost_effectiveness
                    })

            # Select most cost-effective model that meets quality requirements
            best_option = max(model_options, key=lambda x: x['effectiveness'])
            optimization_plan[use_case] = best_option

        return optimization_plan

    def implement_intelligent_caching(self) -> dict:
        """Implement caching strategy to reduce API costs"""

        caching_strategy = {
            'similar_posts_cache': {
                'similarity_threshold': 0.85,
                'cache_duration_hours': 24,
                'estimated_cost_savings': 0.40  # 40% reduction
            },
            'template_response_cache': {
                'pattern_match_threshold': 0.90,
                'cache_duration_hours': 72,
                'estimated_cost_savings': 0.60  # 60% reduction for template matches
            },
            'context_analysis_cache': {
                'subreddit_context_cache_hours': 168,  # 1 week
                'user_profile_cache_hours': 24,
                'estimated_cost_savings': 0.25  # 25% reduction
            }
        }

        return caching_strategy

# docker-compose-enterprise.yml
version: '3.8'

services:
  # Load balancer
  nginx:
    image: nginx:alpine
    ports:
      - "80:80"
      - "443:443"
    volumes:
      - ./nginx.conf:/etc/nginx/nginx.conf

  # API Gateway
  api-gateway:
    image: reddit-automation-gateway
    replicas: 2
    environment:
      - REDIS_CLUSTER=redis-cluster:6379

  # Specialized monitoring services
  keyword-monitor:
    image: reddit-automation-monitor
    replicas: 3
    environment:
      - SERVICE_TYPE=keyword_monitor
      - KAFKA_BROKERS=kafka:9092

  subreddit-monitor:
    image: reddit-automation-monitor
    replicas: 5
    environment:
      - SERVICE_TYPE=subreddit_monitor
      - KAFKA_BROKERS=kafka:9092

  # AI processing services
  response-generator:
    image: reddit-automation-ai
    replicas: 10
    environment:
      - AI_MODEL_PRIMARY=claude-3-sonnet
      - AI_MODEL_FALLBACK=gpt-4o-mini
      - PROCESSING_QUEUE=response_generation

  quality-controller:
    image: reddit-automation-quality
    replicas: 3
    environment:
      - QUALITY_THRESHOLD=0.75
      - PROCESSING_QUEUE=quality_control

  # Account management
  account-manager:
    image: reddit-automation-accounts
    replicas: 2
    environment:
      - ACCOUNT_POOL_SIZE=20
      - ROTATION_STRATEGY=geographic

  # Data infrastructure
  postgres-cluster:
    image: postgres:13
    environment:
      - POSTGRES_DB=reddit_automation
      - POSTGRES_REPLICATION=master

  redis-cluster:
    image: redis:6-alpine
    command: redis-server --appendonly yes --cluster-enabled yes

  kafka:
    image: confluentinc/cp-kafka
    environment:
      - KAFKA_ZOOKEEPER_CONNECT=zookeeper:2181
      - KAFKA_LISTENERS=PLAINTEXT://0.0.0.0:9092

  # Monitoring and observability
  prometheus:
    image: prom/prometheus
    volumes:
      - ./prometheus-enterprise.yml:/etc/prometheus/prometheus.yml

  grafana:
    image: grafana/grafana
    environment:
      - GF_SECURITY_ADMIN_PASSWORD=${GRAFANA_PASSWORD}
    ports:
      - "3000:3000"

enterprise_metrics = {
    "volume_metrics": {
        "posts_processed_daily": 5000,
        "responses_generated_daily": 1500,
        "successful_engagements_daily": 1200,
        "accounts_actively_managed": 25
    },

    "quality_metrics": {
        "average_upvote_ratio": 0.78,
        "response_approval_rate": 0.85,
        "community_acceptance_rate": 0.92,
        "account_health_score": 0.89
    },

    "business_impact": {
        "qualified_leads_monthly": 2400,
        "conversion_rate_reddit_traffic": 0.08,
        "customer_acquisition_cost_reduction": 0.65,
        "brand_mention_sentiment_improvement": 0.34
    },

    "operational_efficiency": {
        "cost_per_engagement": 0.12,
        "human_oversight_hours_daily": 2.5,
        "automation_coverage_percentage": 0.88,
        "system_uptime": 0.9995
    }
}