# Personalized Product Recommendations that convert

Lift AOV and repeat purchases with AI-driven recommendations tailored to each shopper across home, PDP, cart, and emails.

## Solutions for every platform

**Right recs, right moment**  
Home, PDP, cart, and post‑purchase surfaces tuned to your funnel.

**Signals that matter**  
Behavior, context, and catalog metadata — not just “people also bought.”

**Guardrails built-in**  
In-stock, margin, compliance, and merchandising rules respected.

**Scale without limits**  
Keep recommendations fast, relevant, and cost-efficient as your goals grow, no re-engineering required.

Get started this week

### Day 1
1. **Goals & Mapping**  
   Identify target surfaces and KPIs.

### Days 2-3
2. **Connect Catalog & Events**  
   Sync products, inventory, and user events.

### Days 4-5
3. **Tune Relevance**  
   Balance revenue, margin, and experience.

### Days 6-7
4. **Go Live**  
   Monitor AOV, CTR; iterate weekly.

### Plugs into your workflow
Integrate via API or batch. Keep your current analytics and experimentation setup, or use Shaped’s built-in tools for end-to-end measurement.

### Engineered for production
Low-latency APIs, SDKs in your language, and enterprise-grade SLAs, documented and ready for engineering teams.

### Build in-house with Shaped
| Aspect                   | Traditional Approach                              | Shaped Approach                   |
|--------------------------|--------------------------------------------------|-----------------------------------|
| **Time to first experiment** | 6–12 months to build infra before testing ideas | ~7 days to run your first experiment |
| **Iteration speed**      | Slow — pipeline changes, retraining, and deployment cycles | Rapid — deploy and measure new experiments instantly |
| **Team required**        | ML engineers, infra engineers, product, data science | 1 engineer to integrate           |
| **Upfront cost**        | $500k–$1M+ in salaries & infra                   | Subscription pricing               |
| **Maintenance**         | Continuous tuning, scaling, bug fixes             | Fully managed by Shaped           |
| **Cold start**          | Weeks or months to gather enough data             | Solved from day one               |
| **Cross-surface learning** | Siloed models per surface                        | One model that learns from every signal |

**Ship Product Recommendations in one sprint**

### Step 1: Connect your data
All you need are user interactions and (optionally) item metadata. No complex data pipelines or feature engineering required.

### Step 2: Configure your feed
Define your feed in minutes with a simple config—no custom ML models or tuning cycles. Shaped handles the hard parts for you.

### Step 3: Deploy automatically
Shaped trains, schedules, and scales your feed to production. Your team ships in weeks, not months—without adding infra or ML hires.

## More Use Cases:
- **Baselines** - Explore rule-based models like Popular, Trending, and Chronological rankings.  
- **Boosting** - Learn how to promote specific items intelligently within personalized rankings.  
- **Cart Upsell & Cross-Sell** - Boost AOV with intelligent upsell and cross-sell recommendations in the shopping cart.  
- **Conversational Recommenders** - Building personalized conversational AI assistant experiences.  
- **"For You" Feeds** - Build dynamic, personalized feeds like TikTok or Instagram Reels.  
- **Grid Ranking** - Create personalized grids like Netflix with ranked rows and columns.  
- **Item Cold Start** - Handle new items intelligently with attribute-aware ranking models.  
- **Who to Follow** - Suggest relevant users to follow on social platforms or marketplaces.  
- **Category Pages** - Re-rank items within categories based on user preferences.  
- **Email** - Deliver personalized recommendations in email campaigns.  
- **Notifications** - Send relevant, personalized notifications to engage users.  
- **Product Detail Pages** - Show similar items on PDPs to keep users engaged.  
- **Product Recs** - Build personalized product recommendations for e-commerce.  
- **Item Reranking** - Re-rank pre-selected items (e.g. from search) based on user preferences.  
- **Hybrid Search** - Combine keyword search with personalized ranking for better results.  
- **Similar Users & Items** - Find similar items or users for personalized recommendations.  
- **User Cold Start** - Provide relevant recommendations for new or anonymous users.  
- **User Interest Ranking** - Rank items based on user attributes, stated interests and session context.  
- **User & Item Embeddings** - Leverage embeddings for advanced recommendation strategies.  
- **Watch Next Carousels** - Suggest the perfect next video to keep users engaged.