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

  1. Connect Index
    Point Shaped to your data source or API.

Days 4-5

  1. Tune Relevance
    Blend lexical/semantic weights, add boosts and filters.

Days 6-7

  1. 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.