From strategy to production system.
Monday: 3 core prompts for community building. Tuesday: automated engagement code. Wednesday: team workflows for community managers. Thursday: complete technical architecture. Moderation AI, engagement engine, ambassador portal, analytics—all integrated for 100K+ member communities.
Key Assumptions
- •Community size: 1K-100K members with 10-50K daily active users
- •Multi-platform: Discord, Slack, custom forums, mobile apps
- •Moderation: Real-time toxic content detection, spam filtering, policy enforcement
- •Engagement: Personalized recommendations, gamification, automated campaigns
- •Ambassador program: 50-500 active ambassadors with tiered rewards
- •Analytics: Real-time dashboards, predictive models, sentiment tracking
- •Compliance: GDPR, CCPA, content moderation laws (DSA in EU)
- •Uptime: 99.9% SLA with <200ms p95 latency for moderation
System Requirements
Functional
- Real-time content moderation across text, images, links (toxic, spam, policy violations)
- Engagement engine: personalized content recommendations, automated campaigns, gamification
- Ambassador portal: application workflows, reward tracking, impact analytics
- Analytics dashboard: member growth, engagement metrics, churn prediction, sentiment analysis
- Multi-platform integration: Discord, Slack, custom APIs, webhooks
- Policy management: versioned policies, A/B testing, gradual rollout
- Notification system: email, push, in-app, SMS for engagement and alerts
Non-Functional (SLOs)
💰 Cost Targets: {"per_member_per_month_usd":0.15,"moderation_per_1k_messages_usd":0.5,"engagement_per_1k_recommendations_usd":0.3}
Agent Layer
planner
L4Decomposes incoming requests, routes to appropriate agents, manages multi-step workflows
🔧 Policy Lookup Service, Member Profile Cache, Queue Manager
⚡ Recovery: If agent unavailable: queue request, return 202 Accepted, If timeout: retry with exponential backoff (3 attempts), If all agents down: route to manual review queue
moderation
L3Real-time content safety: toxicity detection, spam filtering, policy enforcement
🔧 Toxicity Classifier (ML model), Perspective API (Google), Spam Detector (rule-based + ML), Image Moderation API (AWS Rekognition)
⚡ Recovery: If ML model fails: fall back to rule-based classifier, If Perspective API down: use local model (slightly lower accuracy), If confidence < 0.7: route to human moderator
engagement
L3Personalized recommendations, automated campaigns, gamification logic
🔧 Recommendation Engine (collaborative filtering), Campaign Manager, Gamification Service, Notification Service
⚡ Recovery: If recommendation model fails: fall back to popularity-based ranking, If notification service down: queue for retry, If member unsubscribes: update preferences, halt campaigns
ambassador
L3Manages ambassador applications, rewards, impact tracking
🔧 Application Scorer (ML model), Activity Tracker, Reward Service (Stripe API), Impact Analytics
⚡ Recovery: If scorer unavailable: route to manual review, If reward service down: queue redemption, notify ambassador, If activity tracking fails: use cached data, flag for reconciliation
analytics
L3Real-time insights, predictive models (churn, sentiment), dashboard generation
🔧 Churn Predictor (XGBoost model), Sentiment Analyzer (fine-tuned BERT), Elasticsearch (aggregations), Data Warehouse (Snowflake/BigQuery)
⚡ Recovery: If ML model fails: return cached predictions with staleness warning, If Elasticsearch down: fall back to SQL queries (slower), If data warehouse unreachable: serve from Redis cache (last 24h)
guardrail
L4Policy enforcement, PII redaction, safety filters before/after agent execution
🔧 PII Detector (AWS Comprehend/Presidio), Policy Engine (OPA), Content Filter (regex + ML)
⚡ Recovery: If PII detector fails: block all processing (safety first), If policy engine unavailable: use cached policy rules, If content filter errors: route to manual review
evaluator
L3Quality checks on agent outputs, validates accuracy, triggers retraining
🔧 Evaluation Dataset Store, Drift Detector (statistical tests), Alerting Service (PagerDuty)
⚡ Recovery: If ground truth unavailable: use proxy metrics (user engagement), If drift detector fails: use manual review sampling, If alerting down: log to S3, send email
ML Layer
Feature Store
Update: Real-time (streaming) + batch (daily refresh)
- • member_engagement_score (7d, 30d rolling avg)
- • message_frequency (hourly, daily)
- • sentiment_history (last 10 messages)
- • toxicity_history (flagged messages count)
- • ambassador_activity_score (referrals, events, content)
- • churn_risk_factors (login frequency, engagement drop)
Model Registry
Strategy: Semantic versioning (major.minor.patch)
- • toxicity_classifier
- • churn_predictor
- • sentiment_analyzer
- • recommendation_ranker
Observability
Metrics
- 📊 moderation_latency_p95_ms
- 📊 moderation_accuracy_percent
- 📊 engagement_ctr_percent
- 📊 churn_prediction_auc
- 📊 api_request_rate
- 📊 error_rate
- 📊 queue_depth
- 📊 ml_inference_latency_ms
- 📊 cost_per_member_usd
Dashboards
- 📈 ops_dashboard
- 📈 ml_dashboard
- 📈 business_metrics_dashboard
- 📈 cost_dashboard
Traces
✅ Enabled
Deployment Variants
🚀 Startup
Infrastructure:
- • Serverless API (Lambda/Cloud Functions)
- • Managed PostgreSQL (RDS/Cloud SQL)
- • Managed Redis (ElastiCache/Memorystore)
- • OpenAI/Anthropic API (no self-hosted models)
- • Perspective API (Google)
- • SendGrid (email)
- • Firebase (push notifications)
- • CloudWatch/Stackdriver (monitoring)
→ Minimal ops overhead: fully managed services
→ Cost-optimized: pay-per-use, no idle resources
→ Fast iteration: deploy in minutes, scale automatically
→ Trade-off: higher per-request cost at scale, vendor lock-in
🏢 Enterprise
Infrastructure:
- • Kubernetes (EKS/GKE) for container orchestration
- • PostgreSQL (self-managed or Aurora/Cloud SQL with private networking)
- • Redis (self-managed or ElastiCache with VPC peering)
- • Self-hosted ML models (GPU instances, SageMaker/Vertex AI)
- • Private LLM endpoints (Azure OpenAI with VNet integration)
- • Custom SMTP server or dedicated SendGrid account
- • APNs/FCM with dedicated credentials
- • Prometheus + Grafana (self-hosted or managed)
- • ELK Stack (self-hosted or managed)
- • SAML/OIDC SSO (Okta, Azure AD)
- • BYO KMS/HSM for encryption keys
- • Multi-region deployment (active-active)
- • VPC isolation, private subnets, NAT gateways
- • DDoS protection (AWS Shield, Cloudflare)
→ Full control: custom networking, security policies, data residency
→ Compliance: SOC 2, ISO 27001, GDPR, HIPAA-ready architecture
→ Cost-optimized at scale: reserved instances, committed use discounts
→ Trade-off: higher ops complexity, longer setup time
📈 Migration: Start with startup stack. At 10K+ members, migrate to K8s for cost savings. At 50K+ members, add multi-region for latency. At 100K+ members, self-host ML models for cost control.
Risks & Mitigations
⚠️ Moderation model bias (over-flagging certain demographics)
Medium✓ Mitigation: Diverse training data (50K+ messages from diverse communities). Regular bias audits (quarterly). Human-in-the-loop for appeals. Transparency reports (monthly).
⚠️ Engagement engine filter bubble (only recommending similar content)
High✓ Mitigation: Exploration-exploitation balance (10% random recommendations). Diversity penalty in ranking algorithm. A/B test different diversity thresholds.
⚠️ Ambassador program fraud (fake referrals, gaming points)
Medium✓ Mitigation: Fraud detection model (XGBoost on activity patterns). Manual review for high-value rewards. Rate limiting on referrals (max 10/day).
⚠️ Data breach (PII exposure)
Low✓ Mitigation: Encryption at rest (AES-256) and in transit (TLS 1.3). PII redaction before LLM. Regular security audits (quarterly). Incident response plan (tested annually).
⚠️ LLM API outage (OpenAI/Anthropic down)
Medium✓ Mitigation: Multi-model failover (GPT-4 → Claude → local model). Local model warm standby. Queue requests for retry. SLA monitoring with alerts.
⚠️ Cost overrun (unexpected traffic spike)
Medium✓ Mitigation: Cost guardrails (daily budget alerts). Auto-scaling limits (max 100 instances). Traffic shaping (rate limiting per user). Cost dashboard (real-time monitoring).
⚠️ Regulatory changes (new content moderation laws)
Medium✓ Mitigation: Policy versioning (easy rollout of new rules). Legal review (quarterly). Compliance dashboard (track policy adherence). Geo-specific policies (EU vs US).
Evolution Roadmap
Phase 1: MVP (0-3 months)
Months 1-3- → Launch basic moderation (toxicity detection)
- → Deploy engagement engine (simple recommendations)
- → Build ambassador portal (application + rewards)
- → Set up analytics dashboard (growth metrics)
Phase 2: Scale (3-6 months)
Months 4-6- → Improve moderation accuracy to 99%+
- → Add personalization to engagement engine
- → Automate ambassador rewards
- → Add predictive models (churn, sentiment)
Phase 3: Optimize (6-12 months)
Months 7-12- → Multi-region deployment (low latency globally)
- → Self-hosted ML models (cost optimization)
- → Advanced analytics (cohort analysis, A/B testing)
- → Enterprise features (SSO, SAML, audit logs)
Complete Systems Architecture
9-layer architecture from user interaction to ML infrastructure
Sequence Diagram - Message Moderation Flow
Community Engagement System - Hub Architecture
7 ComponentsCommunity Engagement System - Feedback Loops
6 ComponentsData Flow - End-to-End Message Processing
Scaling Patterns
Key Integrations
Discord Integration
Slack Integration
Email Campaigns (SendGrid/SES)
Push Notifications (FCM/APNs)
Analytics (Segment)
Security & Compliance
Failure Modes & Recovery
| Failure | Fallback | Impact | SLA |
|---|---|---|---|
| Moderation Agent (ML model) down | Fall back to rule-based classifier (lower accuracy but functional) | Degraded accuracy (99.5% → 95%), but community still protected | 99.5% uptime |
| Perspective API (Google) rate limit exceeded | Use local toxicity model (PyTorch), queue requests for retry | Slight latency increase (80ms → 120ms), no user-facing errors | 99.9% uptime |
| Database (PostgreSQL) primary down | Promote read replica to primary, route writes to new primary | 30-60 sec downtime for writes, reads unaffected | 99.9% uptime |
| Kafka (event streaming) partition unavailable | Buffer events in Redis, replay when Kafka recovers | Analytics delayed by 5-10 min, no data loss | 99.5% uptime |
| Engagement Agent (recommendation model) returns low-quality results | Evaluator Agent detects low quality, falls back to popularity-based ranking | Lower CTR (15% → 10%), but recommendations still served | 99.0% uptime |
| Discord API rate limit exceeded | Queue messages, send in batches with exponential backoff | Delayed moderation actions (1-2 min), but no messages lost | 99.5% uptime |
| PII detection service (AWS Comprehend) down | Block all processing (safety first), queue for retry | Moderation paused, manual review required | 100% data safety (no PII leakage) |
Advanced ML/AI Patterns
Production ML engineering beyond basic API calls