AI Personalization Engine
Show every visitor the products, content, and offers most likely to convert.
- Project range
- $8,000–18,000
- AWS running cost
- $80–380/mo
- Time to deploy
- 4–6 weeks
- Best-fit industries
- E-commerce, Retail
Executive summary
A recommendation and personalization service that learns from behavior and catalog data to deliver individualized product, content, and offer suggestions in real time — on the site, in email, and in-app. It replaces one-size-fits-all merchandising with relevance tuned to each visitor, lifting conversion, order value, and engagement.
Business problem
Most small and mid-size sites show everyone the same products and messages, leaving revenue on the table. Manual merchandising can't adapt to each visitor or keep up with a changing catalog, and generic email blasts underperform because they ignore individual intent.
Architecture
AWS services
Amazon API Gateway
Networking- — Real-time recommendation endpoint
- — Rate limiting
Amazon Personalize
AI / ML- — Train and serve individualized recommendations
- — Real-time and batch inference
AWS Lambda
Compute- — Assemble context and call recommenders
- — Apply business rules and merchandising overrides
Amazon DynamoDB
Database- — Visitor profiles and session context
Amazon S3
Storage- — Interaction history and catalog data for training
Amazon EventBridge
Messaging- — Stream behavior events for continuous learning
Web / email / app adapters
Integration- — Deliver recommendations across channels
Amazon CloudWatch
Observability- — Logs, metrics, and cost alarms
Data flow
- 1
Behavior events and catalog data stream via EventBridge into an S3 dataset for training.
- 2
Amazon Personalize trains recommenders and serves real-time suggestions.
- 3
On each request, Lambda assembles visitor context from DynamoDB and calls the recommender.
- 4
Business rules and merchandising overrides apply before results are returned.
- 5
Recommendations are delivered across web, email, and app, and new interactions feed back to improve the models.
Security considerations
- Behavioral and profile data encrypted at rest and in transit.
- Privacy-aware profiling with consent and opt-out honored.
- Least-privilege IAM; channel credentials in Secrets Manager.
- Merchandising guardrails prevent inappropriate or out-of-stock suggestions.
Cost considerations
- Amazon Personalize training and inference is the main variable cost.
- Batch recommendations for email reduce per-request cost.
- S3, DynamoDB, and EventBridge are inexpensive at rest.
Scalability
- Serverless and managed; scales to high traffic and large catalogs.
- New channels and recommenders attach without core rewrites.
- Rules and objectives tunable per campaign and segment.
Deployment roadmap
Phase 1 — Data & goals
Weeks 1–2- — Connect interaction and catalog data; set objectives
- — Provision AWS foundation and event stream
Phase 2 — Train & build
Weeks 3–5- — Train recommenders and build the API
- — Wire web, email, and app delivery
Phase 3 — Launch & optimize
Week 6- — A/B test against baseline merchandising
- — Tune objectives and guardrails
Future enhancements
- Personalized search ranking and results.
- Next-best-offer and dynamic bundle suggestions.
- Cross-channel journey orchestration.
- Uplift and revenue-attribution reporting into the executive dashboard.