From manual spreadsheets to autonomous pricing intelligence.
Monday: 3 pricing prompts (monitor, optimize, test). Tuesday: automation code. Wednesday: team workflows. Thursday: complete technical architecture. Four specialized agents, real-time competitor data, ML optimization engine, and A/B testing platform that scales from 10 to 10,000 SKUs.
Key Assumptions
- •Monitor 10-1000 competitor products (hourly scrapes for critical SKUs)
- •Optimize pricing for 10-10,000 SKUs (daily batch + real-time overrides)
- •Run 5-50 concurrent A/B tests (statistical significance in 3-7 days)
- •Integrate with Stripe/payment gateway for revenue tracking
- •GDPR/SOC2 compliance for customer data and pricing algorithms
- •Multi-currency support (USD, EUR, GBP + 10 others)
- •API-first: headless pricing engine for web/mobile/POS
System Requirements
Functional
- Real-time competitor price monitoring (scraping + API integrations)
- ML-driven price optimization (demand forecasting, elasticity modeling)
- A/B testing platform (statistical significance, revenue attribution)
- Dynamic pricing rules engine (time-based, inventory-based, segment-based)
- Revenue analytics dashboard (lift analysis, margin tracking)
- Multi-channel pricing (web, mobile, B2B portals, marketplace sync)
- Audit trail (every price change logged with reasoning)
Non-Functional (SLOs)
💰 Cost Targets: {"per_sku_per_month_usd":0.5,"per_competitor_scrape_usd":0.02,"ml_inference_per_1k_usd":0.1}
Agent Layer
planner
L4Orchestrates pricing workflow: monitor → optimize → test → apply
🔧 Monitor Agent, Optimizer Agent, Test Manager Agent, Evaluator Agent
⚡ Recovery: If Monitor fails: use cached competitor data (max 24h old), If Optimizer fails: fallback to rule-based pricing, If Test Manager fails: apply price without A/B test
monitor
L2Scrapes competitor prices, validates data quality
🔧 Scraper Pool API, Price Intelligence API, Data Validator
⚡ Recovery: If scrape fails: retry 3x with backoff, If all fail: use Price Intelligence API, If both fail: use last known prices (flagged as stale)
optimizer
L3ML-driven price recommendations with elasticity modeling
🔧 ML Inference Engine, Feature Store, Revenue Simulator
⚡ Recovery: If ML inference fails: use rule-based optimization (margin + competitor avg), If confidence < 0.6: flag for human review, If elasticity model unavailable: use historical avg
test_manager
L3A/B test orchestration, statistical analysis, winner selection
🔧 Statistical Engine, Revenue Tracker, Test Config DB
⚡ Recovery: If test fails to reach significance: extend duration or stop, If revenue tracking fails: pause test, investigate, If variant severely underperforms: early stop (safety)
evaluator
L2Quality checks: price sanity, margin validation, compliance
🔧 Margin Calculator, Policy Engine, Anomaly Detector
⚡ Recovery: If margin too low: reject, suggest min price, If price anomaly detected: flag for review, If policy violation: block and alert
guardrail
L2Safety filters: prevent price gouging, protect margins, enforce limits
🔧 Price Bounds Checker, Market Context Analyzer, Compliance Rules
⚡ Recovery: If price > 2x competitor avg: cap at 1.5x, If margin < 10%: raise to min margin, If price change > 20%: require approval
ML Layer
Feature Store
Update: Hourly for real-time features, daily for batch features
- • competitor_price_avg_7d
- • competitor_price_min_max
- • historical_demand_30d
- • price_elasticity_coefficient
- • inventory_level
- • seasonality_factor
- • customer_segment_willingness_to_pay
- • conversion_rate_at_price_point
- • margin_percent
- • days_since_last_price_change
Model Registry
Strategy: Semantic versioning with A/B testing for new versions
- • demand_forecaster
- • elasticity_estimator
- • price_optimizer
Observability
Metrics
- 📊 scrape_success_rate
- 📊 scrape_latency_p95_ms
- 📊 ml_inference_latency_ms
- 📊 price_recommendation_confidence_avg
- 📊 ab_test_decision_latency_ms
- 📊 api_latency_p95_ms
- 📊 revenue_lift_percent
- 📊 margin_realization_percent
- 📊 guardrail_trigger_rate
- 📊 policy_violation_count
Dashboards
- 📈 ops_dashboard
- 📈 ml_dashboard
- 📈 revenue_dashboard
- 📈 ab_test_dashboard
Traces
✅ Enabled
Deployment Variants
🚀 Startup
Infrastructure:
- • Single-region deployment (AWS us-east-1 or GCP us-central1)
- • Managed services: RDS PostgreSQL, ElastiCache Redis, Lambda/Cloud Run
- • Serverless scraping (AWS Fargate spot instances)
- • ML inference: SageMaker serverless or Vertex AI
- • Monitoring: CloudWatch + Grafana Cloud (free tier)
→ Cost-optimized: ~$500/mo for 1K SKUs
→ Quick to deploy: 2-3 weeks
→ Manual oversight for edge cases
→ No multi-tenancy (single customer)
🏢 Enterprise
Infrastructure:
- • Multi-region deployment (US + EU + APAC)
- • Kubernetes (EKS/GKE) with autoscaling
- • Private VPC with VPN/Direct Connect
- • Multi-tenant architecture (customer isolation)
- • BYO KMS for encryption keys
- • SSO/SAML integration (Okta/Azure AD)
- • Dedicated ML infrastructure (GPU instances)
- • Data residency controls (per-customer region)
→ Cost: $8K+/mo for 10K+ SKUs
→ SLA: 99.99% uptime
→ Full audit trail + compliance reports
→ 24/7 support + dedicated success manager
📈 Migration: Start with startup variant. Migrate to enterprise when: (1) >5K SKUs, (2) Multi-region required, (3) Enterprise customers demand SSO/audit, (4) Revenue >$10M/yr. Migration path: (a) Deploy K8s cluster, (b) Migrate DB to multi-region setup, (c) Add SSO integration, (d) Implement multi-tenancy, (e) Cutover with blue-green deployment.
Risks & Mitigations
⚠️ Competitor scraping blocked (rate limits, legal)
High✓ Mitigation: Multi-layered: (1) Rotate proxies, (2) Use Price Intelligence API as backup, (3) Cache data for 24h, (4) Legal review of scraping ToS, (5) Consider partnerships with data providers.
⚠️ ML model drift (market conditions change)
Medium✓ Mitigation: Weekly drift detection (KL divergence on features), auto-retrain if drift >0.1, A/B test new models before rollout, human review for large price changes.
⚠️ Price wars (competitors aggressively undercut)
Medium✓ Mitigation: Guardrail Agent enforces min margin (10%), alerts if competitor prices drop >20%, human approval for defensive pricing, consider non-price differentiation.
⚠️ A/B tests fail to reach significance (low traffic)
Medium✓ Mitigation: Pre-test power analysis (sample size calculator), use multi-armed bandit for faster convergence, extend test duration or increase traffic allocation.
⚠️ Pricing algorithm bias (discriminatory pricing)
Low✓ Mitigation: Guardrail Agent blocks segment-based pricing, audit trail for all price changes, regular bias audits (quarterly), legal review of pricing policies.
⚠️ Data breach (competitor data, revenue metrics)
Low✓ Mitigation: Encryption at rest (KMS), encryption in transit (TLS 1.3), RBAC (least privilege), audit logs (immutable), SOC2 compliance, regular pen testing.
⚠️ System outage during peak sales (Black Friday)
Low✓ Mitigation: Multi-region deployment, auto-scaling, cache layer (Redis), fallback to static prices, load testing (10x expected traffic), 24/7 on-call during peak.
Evolution Roadmap
Phase 1: MVP (0-3 months)
Weeks 1-12- → Launch basic competitor monitoring (100 SKUs)
- → Rule-based pricing (margin + competitor avg)
- → Manual A/B testing (5 tests)
- → Basic analytics dashboard
Phase 2: ML-Powered (3-6 months)
Months 4-6- → Deploy ML demand forecasting (1K SKUs)
- → Automated A/B testing (20 concurrent tests)
- → Real-time pricing API (<100ms)
- → Advanced analytics (revenue lift, elasticity)
Phase 3: Enterprise Scale (6-12 months)
Months 7-12- → Scale to 10K+ SKUs
- → Multi-region deployment (US + EU)
- → Multi-tenant architecture
- → 99.99% uptime SLA
- → SOC2 compliance
Complete Systems Architecture
9-layer architecture: Presentation → Agents → ML → Data
Sequence Diagram - Price Optimization Request
Dynamic Pricing System - Hub Orchestration
7 ComponentsDynamic Pricing System - Feedback Loops & Optimization
7 ComponentsData Flow - Price Optimization Cycle
Hourly monitoring → Daily optimization → Continuous A/B testing
Scaling Patterns
Key Integrations
Stripe Payment Gateway
Competitor Scraping
Price Intelligence SaaS (Prisync/Competera)
Analytics Platform (Segment/Mixpanel)
Security & Compliance
Failure Modes & Fallbacks
| Failure | Fallback | Impact | SLA |
|---|---|---|---|
| Competitor scraping fails (rate limit, site down) | Use Price Intelligence API as backup, then cached data (max 24h old) | Degraded freshness, not broken | 99.5% scrape success rate |
| ML inference timeout or model error | Fallback to rule-based pricing (margin + competitor avg) | Lower revenue lift (10% vs 20%), quality maintained | 99.9% inference availability |
| A/B test fails to reach significance | Extend test duration or stop test and revert to control | Delayed rollout, no revenue loss | 80% tests reach significance in 7 days |
| Database unavailable (outage) | Read from replica (eventual consistency), cache layer serves stale prices | Read-only mode, no new price changes | 99.99% DB availability (RDS Multi-AZ) |
| Pricing API latency spike (>500ms) | Serve cached prices, bypass ML inference | Stale prices (max 1 hour), fast response | p95 latency <100ms |
| Guardrail agent detects price gouging (price >2x competitor avg) | Block price change, alert pricing admin, require manual approval | Safety first, prevents brand damage | 100% guardrail enforcement |
| Revenue tracking fails (Stripe webhook missed) | Batch reconciliation (hourly), query Stripe API for missing events | Delayed revenue attribution, eventual consistency | 99.9% webhook delivery |
Advanced ML/AI Patterns
Production ML engineering for pricing optimization