From prompts to production SEO intelligence system.
Monday: 3 SEO prompts (competitor analysis, content gaps, keyword tracking). Tuesday: automated agents. Wednesday: team workflows. Thursday: complete production architecture. Multi-agent orchestration, ML pipelines, SERP monitoring, and scaling to 10K+ keywords daily.
Key Assumptions
System Requirements
Functional
- Competitor SERP position tracking with historical trends
- Content gap detection using semantic similarity and keyword clustering
- Keyword opportunity scoring based on difficulty, volume, and relevance
- Automated content brief generation with structure, headings, and word count
- Backlink profile analysis and link-building opportunity identification
- Real-time alerts for ranking changes and competitor content updates
- Natural language query interface for strategy questions
Non-Functional (SLOs)
💰 Cost Targets: {"per_keyword_per_month_usd":0.5,"per_competitor_per_month_usd":5,"llm_cost_per_analysis_usd":0.15}
Agent Layer
planner
L4Decomposes user query into sub-tasks, selects appropriate tools and agents
🔧 Query parser, Task decomposer, Agent registry
⚡ Recovery: If query ambiguous → request clarification, If no matching agent → fallback to manual queue
scraper_executor
L2Fetches SERP data, competitor pages, backlink profiles via APIs and web scraping
🔧 SERP API (SEMrush, Ahrefs), Web scraper (Playwright/Puppeteer), Proxy rotation service, Rate limiter
⚡ Recovery: If API rate limit → queue for retry with backoff, If scrape blocked → rotate proxy + retry, If data incomplete → flag for manual review
analysis_executor
L3Analyzes SERP data, detects content gaps, scores keyword opportunities
🔧 Feature extraction pipeline, Semantic similarity model, Ranking prediction model, Clustering algorithm
⚡ Recovery: If low confidence score → request human review, If model drift detected → fallback to rule-based heuristics
content_generator
L3Generates content briefs, outlines, and optimization recommendations
🔧 LLM (GPT-4, Claude), Prompt templates, Content scoring model
⚡ Recovery: If LLM hallucination detected → regenerate with stricter prompt, If brief quality low → escalate to human editor
evaluator
L3Validates output quality, checks for hallucinations, enforces business rules
🔧 Evaluation rubric, Fact-checking model, Business rule engine
⚡ Recovery: If quality below threshold → reject + request regeneration, If critical error → alert human operator
guardrail
L4Enforces safety, compliance, and policy constraints across all operations
🔧 PII detection model, Rate limiter, Policy database, Audit logger
⚡ Recovery: If policy violation → block operation + alert admin, If PII detected → redact + log incident
ML Layer
Feature Store
Update: Hourly for critical keywords, daily for long-tail
- • keyword_volume_30d_avg
- • keyword_difficulty_score
- • serp_feature_presence (featured_snippet, people_also_ask, etc.)
- • competitor_authority_score
- • content_semantic_similarity
- • backlink_count_delta
- • ranking_velocity (change rate)
- • user_intent_classification (informational, transactional, navigational)
Model Registry
Strategy: Semantic versioning with A/B testing for production rollout
- • keyword_opportunity_scorer
- • content_gap_detector
- • ranking_predictor
Observability Stack
Real-time monitoring, tracing & alerting
0 activeDeployment Variants
Startup Architecture
Fast to deploy, cost-efficient, scales to 100 competitors
Infrastructure
Risks & Mitigations
⚠️ SERP API rate limits exceeded during peak usage
High (daily occurrence at scale)✓ Mitigation: Multi-provider failover (SEMrush → Ahrefs → custom scraper). Queue requests with exponential backoff. Cache recent SERP data (1h TTL). Alert if >10 failures/hour.
⚠️ LLM hallucinations in content briefs
Medium (0.5% of outputs)✓ Mitigation: Multi-layer validation: confidence scores, fact-checking model, human review queue. Block outputs with score < 0.8. Continuous eval with human feedback loop.
⚠️ Model drift as SEO landscape changes
High (quarterly algorithm updates)✓ Mitigation: Automated drift detection (KL divergence, accuracy monitoring). Retrain pipeline triggered by drift or quarterly schedule. A/B test new models. Rollback policy.
⚠️ Competitor detects scraping, blocks IPs
Medium (happens monthly)✓ Mitigation: Residential proxy rotation. Respect robots.txt. Add random delays. Fallback to SERP APIs. Legal review of scraping practices.
⚠️ PII leak in scraped content or outputs
Low (0.1% of content)✓ Mitigation: Guardrail agent with PII detection (AWS Comprehend). Automatic redaction. Block outputs with PII. Audit logs. Incident response plan. Encrypt all data at rest.
⚠️ Database failure during peak traffic
Low (1-2x per year)✓ Mitigation: Multi-AZ deployment. Automated failover to read replica. Write queue for eventual consistency. RTO <15min. Regular disaster recovery drills.
⚠️ Cost overrun from LLM API usage
Medium (can spike 2-3x)✓ Mitigation: Cost guardrails: max $X per customer per month. Cache LLM outputs (24h TTL). Use smaller models for simple tasks. Alert if cost >120% of budget. Monthly cost review.
Evolution Roadmap
Progressive transformation from MVP to scale
Phase 1: MVP (0-3 months)
Phase 2: Scale (3-6 months)
Phase 3: Enterprise (6-12 months)
Complete Systems Architecture
End-to-end layer view: 9 layers from UI to security
Presentation
4 components
API Gateway
4 components
Agent Layer
6 components
ML Layer
5 components
Integration
4 components
Data
4 components
External
4 components
Observability
4 components
Security
4 components
Sequence Diagram - Keyword Analysis Request
Automated data flow every hour
Data Flow - End-to-End
User query → actionable SEO strategy in 12 seconds
Key Integrations
SERP APIs (SEMrush, Ahrefs, Moz)
CMS Integration (WordPress, Webflow, HubSpot)
Analytics (Google Analytics 4, Adobe Analytics)
Social APIs (Twitter, LinkedIn, Reddit)
Security & Compliance
Failure Modes & Recovery
| Failure | Fallback | Impact | SLA |
|---|---|---|---|
| SERP API rate limit exceeded | → Queue requests with exponential backoff → Switch to secondary provider (Ahrefs if SEMrush fails) → Custom scraper as last resort | Delayed data (5-15 min), no data loss | 99.5% |
| LLM API timeout/error | → Retry with different model (GPT-4 → Claude → Gemini) → If all fail, queue for manual review | Degraded quality, increased latency | 99.0% |
| Low confidence content brief | → Evaluator flags for human review → Editor queue with context | Quality maintained, throughput reduced | 99.9% |
| Database connection lost | → Read from replica → Write to queue → Sync when primary recovers | Read-only mode, eventual consistency | 99.9% |
| Scraper blocked by anti-bot | → Rotate proxy → Add delay → Use residential proxies → Fallback to API | Increased cost, slower scraping | 98.0% |
| Model drift detected (accuracy drop >5%) | → Rollback to previous model version → Retrain with recent data → A/B test new model | Temporary accuracy loss, auto-recovery | 99.5% |
| PII leak in output | → Guardrail agent blocks output → Alert security team → Audit logs reviewed | Zero PII exposure, some outputs blocked | 100% (safety first) |
RAG vs Fine-Tuning Decision
Hallucination Detection
Evaluation Framework
Dataset Curation
Agentic RAG
Model Drift Detection
Tech Stack Summary
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