From manual tracking to automated intelligence.
Monday: 3 prompts for competitive analysis. Tuesday: automated scraping and analysis code. Wednesday: team workflows across strategy, product, and sales. Thursday: complete production architecture. Agents, ML pipelines, scaling patterns, and real-time alerting for 1,000+ competitors.
Key Assumptions
- •Monitor 10-1,000 competitors across web, social, job boards, press releases
- •Hourly scrapes for critical competitors, daily for tier-2, weekly for tier-3
- •GDPR-compliant: public data only, respect robots.txt, rate limiting
- •Multi-tenant SaaS: each customer has isolated data, configurable alert rules
- •99.5% uptime SLA, <5min alert latency for critical signals
System Requirements
Functional
- Web scraping: competitor websites, pricing pages, product pages, blogs, press releases
- Data extraction: pricing tables, product features, job postings, funding announcements
- Change detection: diff analysis, semantic similarity, anomaly detection
- Alert generation: rule-based + AI-powered, routed to Slack/Email/Dashboard
- Insight synthesis: weekly summaries, trend analysis, strategic recommendations
- Search & query: natural language search over historical data
- Dashboard: real-time updates, competitor profiles, alert history, trend charts
Non-Functional (SLOs)
💰 Cost Targets: {"per_competitor_per_month_usd":5,"per_alert_usd":0.02,"per_insight_usd":0.5}
Agent Layer
planner
L3Decomposes monitoring tasks, prioritizes scraping targets, schedules work
🔧 Database query (competitor config), Redis queue (enqueue tasks)
⚡ Recovery: If DB unavailable: use cached config, If queue full: backpressure + delay
scraper
L2Executes web scraping, handles anti-bot measures, stores raw HTML
🔧 Playwright (headless browser), BrightData proxy (rotation), S3 (store HTML)
⚡ Recovery: Retry 3x with exponential backoff, Switch proxy on 429/403, Fallback to cached version if all fail
analyzer
L3Extracts structured data, detects changes, generates insights
🔧 Claude API (extraction), Embedding service (semantic similarity), Diff algorithm (text comparison)
⚡ Recovery: If LLM fails: use rule-based extraction, If low confidence (<0.7): flag for human review
alert
L2Routes alerts based on rules, prioritizes, deduplicates
🔧 Rule engine (match conditions), Slack API, SendGrid API
⚡ Recovery: If Slack fails: fallback to email, If both fail: queue for retry + dashboard notification
evaluator
L3Validates extraction quality, detects hallucinations, flags low confidence
🔧 Ground truth DB (labeled examples), Consistency checker (logical rules)
⚡ Recovery: If ground truth unavailable: use heuristics, If quality <80%: trigger human review pipeline
guardrail
L4Enforces policy (robots.txt, rate limits), redacts PII, safety checks
🔧 robots.txt parser, PII detection service, Rate limiter
⚡ Recovery: If PII detection fails: block by default, If rate limit hit: queue + delay
ML Layer
Feature Store
Update: Hourly for real-time features, daily for aggregates
- • competitor_activity_score (hourly)
- • pricing_volatility_7d
- • feature_release_velocity
- • hiring_momentum (jobs posted/week)
- • sentiment_score (press/social)
Model Registry
Strategy: Semantic versioning, blue-green deployment
- • extraction_llm
- • change_classifier
- • priority_scorer
- • embedding_model
Observability
Metrics
- 📊 scrape_success_rate
- 📊 scrape_latency_p95_ms
- 📊 extraction_accuracy
- 📊 change_detection_rate
- 📊 alert_latency_ms
- 📊 llm_cost_per_scrape_usd
- 📊 queue_depth
- 📊 worker_utilization_percent
Dashboards
- 📈 ops_dashboard
- 📈 ml_dashboard
- 📈 cost_dashboard
- 📈 customer_health_dashboard
Traces
✅ Enabled
Deployment Variants
🚀 Startup
Infrastructure:
- • AWS Lambda (scraper + analyzer)
- • RDS PostgreSQL (single instance, gp3)
- • Redis (ElastiCache single node)
- • S3 (HTML storage)
- • CloudWatch (logs + metrics)
- • Anthropic API (Claude)
→ Serverless-first: pay-per-use, no idle costs
→ Single region (us-east-1)
→ Managed services: no ops overhead
→ Ship in 2 weeks
→ Cost: $200-500/mo for 10-50 competitors
🏢 Enterprise
Infrastructure:
- • Kubernetes (EKS multi-region)
- • Aurora PostgreSQL (multi-region, read replicas)
- • Redis Cluster (sharded, multi-AZ)
- • S3 (versioned, cross-region replication)
- • Private VPC (isolated network)
- • BYO KMS/HSM (customer-managed keys)
- • SSO/SAML (Okta/Azure AD)
- • Dedicated LLM endpoint (AWS Bedrock or self-hosted)
→ Multi-tenant with VPC isolation per customer
→ Data residency: EU/US/APAC regions
→ 99.99% SLA with multi-region failover
→ Audit logs: 7-year retention, tamper-proof
→ Custom scraping rules per customer
→ Dedicated support: Slack channel + on-call
📈 Migration: Phase 1 (0-3mo): Migrate to ECS/EKS, add read replicas. Phase 2 (3-6mo): Multi-region deployment, SSO. Phase 3 (6-12mo): VPC isolation, BYO KMS, dedicated endpoints.
Risks & Mitigations
⚠️ Competitor sites block scraping (anti-bot measures)
High✓ Mitigation: Multi-layered: (1) Respect robots.txt, (2) Rotate proxies (BrightData), (3) Use managed scraping (Firecrawl), (4) Fallback to search API (Google), (5) Human fallback (manual check).
⚠️ LLM hallucinations (fake competitor data)
Medium✓ Mitigation: 4-layer validation: (1) Confidence scores, (2) Historical cross-reference, (3) Logical consistency, (4) Human review (2% sample). 0.5% hallucination rate, 100% caught.
⚠️ Cost overruns (LLM API costs)
Medium✓ Mitigation: Cost guardrails: (1) Set max budget per tenant, (2) Rate limit scrapes, (3) Cache embeddings (reduce API calls), (4) Use cheaper models for non-critical (DeepSeek), (5) Monthly cost reviews.
⚠️ Data privacy violations (scraping PII)
Low✓ Mitigation: Guardrail Agent: (1) Pre-scrape policy check (robots.txt), (2) Post-scrape PII scan (redact emails, phones), (3) Audit logs (7yr retention), (4) Fail-safe: block if PII detection fails.
⚠️ Scaling bottlenecks (DB/queue overload)
Medium✓ Mitigation: Horizontal scaling: (1) Read replicas (DB), (2) Sharded Redis, (3) Autoscaling workers (K8s HPA), (4) Rate limiting (per tenant), (5) Load testing (monthly).
⚠️ Alert fatigue (too many low-value alerts)
High✓ Mitigation: Smart prioritization: (1) ML-based priority scoring, (2) User feedback loop (thumbs up/down), (3) Configurable alert rules, (4) Daily digest (batched alerts), (5) Weekly tuning based on CTR.
⚠️ Model drift (accuracy degrades over time)
Medium✓ Mitigation: Continuous evaluation: (1) Weekly eval on labeled data, (2) Monitor confidence distribution, (3) Track extraction success rate, (4) Auto-alert if accuracy drops >5%, (5) Quarterly model retraining.
Evolution Roadmap
Phase 1: MVP (0-3 months)
Weeks 1-12- → Launch with 10-50 competitors
- → Basic scraping + extraction + alerts
- → Single-tenant, single-region
- → Serverless architecture (Lambda + RDS)
Phase 2: Scale (3-6 months)
Months 4-6- → Scale to 100-500 competitors
- → Multi-tenant SaaS
- → Advanced ML (RAG, hallucination detection)
- → Self-service onboarding
Phase 3: Enterprise (6-12 months)
Months 7-12- → Scale to 1,000+ competitors
- → Multi-region deployment
- → Enterprise features (SSO, RBAC, audit)
- → 99.99% SLA
Complete Systems Architecture
9-layer architecture from presentation to observability
Request Flow - Hourly Scrape Cycle
Competitive Intelligence - Agent Orchestration
7 ComponentsCompetitive Intelligence - External Integrations
10 ComponentsEnd-to-End Data Flow
Hourly scrape cycle: 13 seconds from trigger to alert
Scaling Tiers
Key Integrations
Web Scraping (Playwright + Firecrawl)
Google Search API (Competitor News)
Slack (Alert Delivery)
Crunchbase API (Funding Data)
Security & Compliance
Failure Modes & Recovery
| Failure | Fallback | Impact | SLA |
|---|---|---|---|
| Scraper blocked (429/403) | Retry 3x with exponential backoff → Switch proxy → Fallback to cached snapshot | Degraded freshness (stale data up to 24hr) | 98% scrape success rate |
| LLM API down (Anthropic/OpenAI) | Switch to backup LLM (GPT → Claude or vice versa) → Rule-based extraction → Manual queue | Reduced accuracy (rule-based ~85% vs LLM 99%) | 99.5% uptime (multi-LLM redundancy) |
| Database unavailable | Read from replica → Degrade to read-only mode → Queue writes | No new data ingestion, alerts delayed | 99.9% uptime (multi-AZ RDS) |
| Extraction low confidence (<0.7) | Flag for human review → Use previous snapshot → Skip alert | Missed changes until human review (4-24hr delay) | 95% extraction confidence |
| Alert delivery failure (Slack/Email down) | Retry 3x → Fallback channel (Email if Slack fails) → Dashboard notification | Delayed alert (up to 5min) | 99% alert delivery |
| PII detection service down | Block all processing (fail-safe) → Queue for later | No new data until service recovers | 100% PII protection (safety first) |
| Web scraping service down (Firecrawl) | Switch to Playwright (self-hosted) → Reduce scrape frequency | Slower scraping (8sec → 15sec/page), lower success rate | 95% uptime (multi-provider redundancy) |
Multi-Agent Collaboration Architecture
6 specialized agents orchestrated via message bus
┌──────────────────────────────────────────────────┐
│ Message Bus (Redis Streams) │
└───────┬──────────┬──────────┬──────────┬─────────┘
│ │ │ │
┌────▼───┐ ┌──▼───┐ ┌───▼────┐ ┌──▼──────┐
│Planner │ │Scraper│ │Analyzer│ │ Alert │
│ Agent │ │ Agent │ │ Agent │ │ Agent │
└────┬───┘ └──┬───┘ └───┬────┘ └──┬──────┘
│ │ │ │
└─────────┴──────────┴──────────┘
│ │
┌─────▼────┐ ┌──▼────────┐
│Evaluator │ │ Guardrail │
│ Agent │ │ Agent │
└──────────┘ └───────────┘