AI Pricing Optimizer
Recommend the right price by product, segment, and moment — with guardrails.
- Project range
- $8,000–18,000
- AWS running cost
- $70–340/mo
- Time to deploy
- 4–6 weeks
- Best-fit industries
- E-commerce, Retail
Executive summary
A pricing engine that recommends optimal prices per product and segment using demand elasticity, cost, inventory, and competitor signals — always within margin and brand guardrails you define. It surfaces the reasoning behind each recommendation and keeps a human in control, replacing static price lists and gut-feel discounting with disciplined, data-driven pricing.
Business problem
Prices are set once and rarely revisited, leaving margin on the table and reactions to competitors slow and inconsistent. Discounting is ad hoc, elasticity is unknown, and there's no systematic way to test what a product could actually command.
Architecture
AWS services
Amazon S3
Storage- — Sales, cost, and competitor data lake
- — Historical price-response data
AWS Lambda
Compute- — Run elasticity models and pricing rules
- — Assemble recommendations within guardrails
Elasticity + rules engine
Compute- — Estimate demand response and optimal price
- — Enforce margin floors and brand guardrails
Amazon Bedrock
AI / ML- — Explain each recommendation and trade-off
- — Generate what-if pricing scenarios
Amazon DynamoDB
Database- — Current recommendations and approval state
Amazon QuickSight
Observability- — Price, margin, and elasticity dashboards
Amazon EventBridge
Messaging- — Schedule pricing runs and change alerts
Store / ERP / PIM adapters
Integration- — Pull data and push approved prices
Amazon CloudWatch
Observability- — Logs, metrics, and cost alarms
Data flow
- 1
Sales, cost, inventory, and competitor data flow into an S3 pricing lake.
- 2
On schedule, the engine estimates elasticity and computes optimal prices within margin and brand guardrails.
- 3
Bedrock explains each recommendation and can generate what-if scenarios for review.
- 4
Recommendations land in DynamoDB and are visualized in QuickSight; a human approves.
- 5
Approved prices are pushed to the store, ERP, or PIM, and outcomes feed back to refine the models.
Security considerations
- Cost and competitor data encrypted and access-controlled.
- Guardrails prevent recommendations outside margin or brand limits.
- Least-privilege IAM; store/ERP credentials in Secrets Manager.
- Human approval and full audit trail before any price change.
Cost considerations
- Model compute and Bedrock rationale generation are the main variable costs.
- Batch, scheduled runs keep compute predictable and low.
- S3, DynamoDB, and EventBridge are inexpensive at rest.
Scalability
- Serverless and batch-oriented; scales across large catalogs.
- New segments, channels, and guardrails are configuration.
- Competitor and cost feeds attach as adapters.
Deployment roadmap
Phase 1 — Data & guardrails
Weeks 1–2- — Connect sales/cost data and set margin guardrails
- — Provision AWS foundation and data lake
Phase 2 — Build & backtest
Weeks 3–5- — Build elasticity models and rules
- — Backtest recommendations against history
Phase 3 — Integrate & launch
Week 6- — Wire price publishing and dashboards
- — Launch with human approval on a subset
Future enhancements
- Real-time dynamic pricing for select channels.
- Automated A/B price experimentation.
- Bundle and promotion optimization.
- Margin and price-realization reporting into the executive dashboard.