# Hybrid Search that finds exactly what users mean

Combine semantic understanding with keyword precision to deliver relevant results on the first try — shipped in days, not months.

## Solutions for every platform

**Understand intent**  
Semantic vectors + rules catch synonyms, typos, and vague queries without hacking the index.

**Keep precision**  
Blend BM25/keyword signals with embeddings so exact matches still win when they should.

**Own the ranking**  
Tune objectives and boosts for your business KPIs and categories.

**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. **Discovery & KPI Mapping**  
Define search success metrics and categories.

### Days 2-3
2. **Connect Index**  
Point Shaped to your data source or API.

### Days 4-5
4. **Tune Relevance**  
Blend lexical/semantic weights, add boosts and filters.

### Days 6-7
6. **Iterate**  
Monitor zero-result rate and 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

### 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

Faster experimentation, proven KPI lift, and no infra burden, so your team can focus on what matters: shipping product, not pipelines.

## Ship Hybrid Search 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.

## One model, every surface

Search learns from browse, cart, and email — and vice-versa. A single model shares user, item, and event signals across surfaces, raising relevance everywhere and reducing zero-result queries over time.

## Explore other 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.
- **Related Content**: Keep users engaged with relevant related content suggestions.
- **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.

# Ready to hit your growth goals?

Get a tailored walkthrough with your KPIs.