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

  1. Connect Catalog & Events
    Sync products, inventory, and user events.

Days 4-5

  1. Tune Relevance
    Balance revenue, margin, and experience.

Days 6-7

  1. 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.