From prompts to production storytelling system.
Monday: 3 core prompts for brand voice, content generation, and quality control. Tuesday: automated story generator. Wednesday: team workflows for marketers, writers, and approvers. Thursday: complete technical architecture. Multi-agent system, voice modeling, CMS integration, and quality assurance for 10,000+ stories monthly.
Key Assumptions
- •Generate 100-10,000 brand stories per month across channels
- •Voice model trained on 500-5,000 existing brand assets
- •CMS integration required (WordPress, Contentful, Sanity, etc.)
- •Human review for high-stakes content (exec comms, PR)
- •Multi-channel output (blog, social, email, ads)
- •Brand guidelines stored as structured data (tone, vocabulary, rules)
- •SOC2 compliance for enterprise customers
System Requirements
Functional
- Voice model captures brand personality and tone
- Content generator produces multi-format stories (blog, social, email)
- Quality control validates brand guidelines and factual accuracy
- CMS integration auto-publishes or queues for review
- Review workflow routes content to appropriate approvers
- Version control tracks content iterations and approvals
- Analytics track performance and voice consistency metrics
Non-Functional (SLOs)
💰 Cost Targets: {"per_story_usd":0.15,"per_1000_stories_usd":150,"infrastructure_monthly_usd":500}
Agent Layer
planner
L4Decomposes content request into tasks, selects tools, routes workflow
🔧 VoiceModelAgent.load(), ContentGeneratorAgent.generate(), QualityControlAgent.validate(), CMSAdapter.publish()
⚡ Recovery: If voice model unavailable → use generic brand guidelines, If generation fails → retry 2x with fallback prompt, If CMS down → queue for manual publish
voice_model
L2Loads brand-specific voice profile, injects tone/vocabulary context
🔧 ModelRegistry.getVoiceModel(brand_id), VectorDB.retrieveExamples(format), FeatureStore.getBrandMetrics()
⚡ Recovery: If model not found → fallback to generic brand guidelines, If vector DB slow → use cached examples
content_generator
L3Generates brand-consistent content using voice model + prompt
🔧 OpenAI.createCompletion(voice_prompt + topic), PromptStore.getTemplate(format), FeatureStore.getAudienceProfile()
⚡ Recovery: If LLM timeout → retry with shorter prompt, If low quality → regenerate with stricter instructions, If API down → queue for batch processing
quality_control
L3Validates content against brand guidelines, checks factual accuracy
🔧 GuidelineValidator.check(content, rules), FactChecker.verify(claims), ReadabilityScorer.analyze(text)
⚡ Recovery: If fact-check API down → flag for manual review, If low score (<0.8) → trigger regeneration, If critical violation → block publish
guardrail
L2Safety checks, PII redaction, policy enforcement
🔧 PIIDetector.scan(content), PolicyEngine.enforce(rules), ToxicityClassifier.score(text)
⚡ Recovery: If PII detected → auto-redact and flag, If policy violation → block publish, If toxicity high → reject and alert
evaluator
L3Post-generation quality assessment, performance tracking
🔧 EvaluationEngine.score(content, metrics), DriftDetector.analyze(scores_over_time), AnalyticsAPI.getEngagement(content_id)
⚡ Recovery: If metrics unavailable → use quality score only, If drift detected → trigger model retraining alert
ML Layer
Feature Store
Update: Daily batch + real-time streaming for engagement metrics
- • brand_voice_embedding (768-dim)
- • historical_content_performance
- • audience_demographics
- • topic_trends
- • format_preferences
- • tone_consistency_score
Model Registry
Strategy: Semantic versioning (major.minor.patch), blue-green deployment
- • VoiceModel
- • QualityClassifier
- • ToxicityDetector
Observability
Metrics
- 📊 story_generation_latency_p95_ms
- 📊 voice_consistency_score
- 📊 quality_score_distribution
- 📊 llm_cost_per_story_usd
- 📊 human_approval_rate_percent
- 📊 guideline_violation_rate
- 📊 cms_publish_success_rate
- 📊 agent_failure_rate
- 📊 cache_hit_rate
- 📊 drift_score
Dashboards
- 📈 ops_dashboard
- 📈 ml_dashboard
- 📈 cost_dashboard
- 📈 quality_dashboard
- 📈 user_activity_dashboard
Traces
✅ Enabled
Deployment Variants
🚀 Startup
Infrastructure:
- • Serverless (Lambda/Cloud Functions)
- • Managed PostgreSQL (RDS/Cloud SQL)
- • Managed Redis (ElastiCache/MemoryStore)
- • OpenAI API (no fine-tuning initially)
- • S3/GCS for media storage
- • CloudWatch/Stackdriver for observability
→ Single region deployment
→ Synchronous processing (simple)
→ No custom fine-tuning (use GPT-4 with prompts)
→ Basic RBAC (3 roles)
→ Cost: $50-200/month for 100-1K stories
→ Deploy in 1-2 weeks
🏢 Enterprise
Infrastructure:
- • Kubernetes (EKS/GKE/AKS)
- • Multi-region PostgreSQL with read replicas
- • Redis cluster (multi-AZ)
- • Fine-tuned GPT-4 + multi-LLM failover
- • Vector DB cluster (Pinecone/Weaviate)
- • Kafka for event streaming
- • VPC with private subnets
- • BYO KMS/HSM for encryption
- • SAML/OIDC with SSO
- • Dedicated Prometheus + Grafana
- • Splunk or ELK for centralized logging
→ Multi-region active-active
→ Private networking (no public IPs)
→ Data residency controls (US/EU/Asia)
→ Advanced RBAC (10+ roles, custom policies)
→ SOC2 Type II compliant
→ 99.9% SLA with disaster recovery
→ Cost: $3,000-10,000/month for 10K+ stories
→ Deploy in 2-3 months
📈 Migration: Start with startup stack. At 1K stories/month, migrate to queue-based. At 5K, introduce Kubernetes and multi-region. At 10K, add Kafka and full enterprise features. Incremental migration with zero downtime.
Risks & Mitigations
⚠️ Voice model drift - Brand evolves, model becomes stale
High (quarterly brand updates)✓ Mitigation: Automated drift detection (weekly). Retrain monthly or when drift >5%. Maintain 10K+ training examples.
⚠️ LLM hallucination - Fake facts, wrong product names
Medium (0.5% rate)✓ Mitigation: 4-layer detection (confidence, fact-check, consistency, human review). Block publish if critical violation. 99% catch rate.
⚠️ CMS integration failure - API down, auth expired
Low (99% uptime)✓ Mitigation: Retry logic (3x with backoff). Queue for manual publish. Multi-CMS support (failover to secondary).
⚠️ Cost overrun - LLM API costs spike with volume
Medium (unpredictable usage)✓ Mitigation: Set cost guardrails ($0.20/story max). Alert if daily spend >$100. Use cheaper models (GPT-3.5) for drafts, GPT-4 for final.
⚠️ PII leakage - Training data contains customer PII
Low (strict data hygiene)✓ Mitigation: PII detection in training pipeline. Auto-redact before fine-tuning. Audit all training data. No customer data in logs.
⚠️ Quality degradation at scale - More volume = lower quality
Medium (common scaling issue)✓ Mitigation: Quality score threshold (0.9+). Human review for low scores. Continuous evaluation (weekly quality reports).
⚠️ Vendor lock-in - Dependent on single LLM provider
High (OpenAI primary)✓ Mitigation: Multi-LLM architecture (GPT-4, Claude, Gemini). Abstract LLM calls behind interface. Test failover monthly.
Evolution Roadmap
Phase 1: MVP (0-3 months)
Weeks 1-12- → Deploy serverless architecture
- → Fine-tune GPT-4 on 1,000 brand assets
- → Integrate with 1 CMS (WordPress)
- → Basic quality control (rule-based)
- → Support 100 stories/month
Phase 2: Scale (3-6 months)
Weeks 13-24- → Migrate to queue-based architecture
- → Fine-tune on 5,000 brand assets
- → Add 2 more CMS integrations (Contentful, Sanity)
- → Advanced quality control (ML classifier)
- → Support 1,000 stories/month
- → Add human review workflow
Phase 3: Enterprise (6-12 months)
Weeks 25-52- → Migrate to Kubernetes multi-region
- → Fine-tune on 10,000 brand assets
- → Multi-LLM failover (GPT-4, Claude, Gemini)
- → Agentic RAG for dynamic context
- → Support 10,000 stories/month
- → SOC2 Type II compliance
- → 99.9% SLA with disaster recovery
Complete Systems Architecture
9-layer view: Presentation to Security
Sequence Diagram - Story Generation Flow
Brand Storytelling - Agent Orchestration
6 ComponentsBrand Storytelling - External Integrations
10 ComponentsData Flow
Content request → Published story in 10 seconds
Scaling Patterns
Key Integrations
CMS Integration (WordPress/Contentful/Sanity)
OpenAI Fine-tuning
Style Guide Database
Review Tools (Slack/Asana/Monday.com)
Security & Compliance
Failure Modes & Fallbacks
| Failure | Fallback | Impact | SLA |
|---|---|---|---|
| LLM API down (OpenAI outage) | Switch to Claude 3.5 Sonnet or Gemini Pro (multi-LLM failover) | Degraded voice consistency (no fine-tuned model), but system operational | 99.5% availability |
| Voice model returns low confidence (<0.7) | Use generic brand guidelines + flag for human review | Quality maintained, slower throughput | 99.9% quality |
| CMS API timeout or down | Queue content for retry (3x with backoff), then manual publish queue | Delayed publish, eventual consistency | 99.0% auto-publish |
| Quality check detects critical guideline violation | Block publish, route to review queue, alert content team | Safety first, no bad content published | 100% compliance |
| PII detected in generated content | Auto-redact PII, flag for review, do not publish | Privacy protected, delayed publish | 100% PII protection |
| Database unavailable (primary down) | Switch to read replica for read operations, queue writes | Read-only mode, no new content generation | 99.9% read availability |
| Vector DB slow or unavailable | Use cached brand examples (Redis), degrade to generic prompts | Lower voice consistency, slower retrieval | 99.5% availability |
Advanced ML/AI Patterns
Production ML engineering beyond basic LLM calls