Personalized Product Recommendations that convert

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

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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.

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Day 1

Goals &  Mapping

Identify target surfaces and KPIs.

Days 2-3

Connect Catalog & Events

Sync products, inventory, and user events.

Days 4-5

Tune Relevance

Balance revenue, margin, and experience.

Days 6-7

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.

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Engineered for production

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

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Build in-house

with Shaped

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 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.

One Model, Every Surface

Recommendations aren’t a silo. A unified model shares signals from browse, search, cart, and email to optimize cross‑surface journey outcomes — AOV, LTV, and repeat purchase — without brittle per‑page logic.