Product Overview

One personalization layer, every surface

Aislegleam attaches to your existing store and personalizes what each shopper sees. No replatforming. No data team required.

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Aislegleam
What Aislegleam does

From raw catalog to personalized storefront

Aislegleam connects to your catalog and behavioral event stream, builds per-session shopper intent models, and delivers ranked product sets to each surface through a lightweight API or tag.

Catalog ingestion

Syncs product feed: SKU attributes, inventory status, pricing. Detects new arrivals and catalog changes within hours of your next feed sync.

Behavioral signal processing

Lightweight JavaScript tag captures views, searches, add-to-cart events, and purchases. Session model updates in real time, no batch delay.

Surface delivery

Ranked product sets returned via REST API, JavaScript widget, or Shopify Liquid tag. Under 200ms p99. Plugs into your existing front-end rendering.

Catalog depth visualization showing product discovery across a large SKU catalog
Platform capabilities

Three surfaces, one integration

Real-time ranked product sets for homepage carousels, PDP related items, and cart cross-sell slots. Combines session-based intent signals with collaborative filtering and catalog affinity scoring. No purchase history required from session 1. Handles cold-start products (new arrivals with no engagement history) via catalog attribute embedding. Explore Recommendations →
Re-ranks your existing search results per session using the same intent model that drives homepage and PDP recommendations. Shoppers who exhibit category affinity see those products surfaced first within their query results. Works with your existing search provider via a re-ranking API call. No reindexing required. Explore Search →
Layer editorial merchandising rules on top of ML ranking. Pin products to positions for seasonal pushes, inventory clearance events, or brand partnership obligations. Rules override ML for specified product sets without disabling personalization for the rest of the catalog. Available on Growth and Scale plans. Explore Merchandising →
Use cases

Where personalization changes the session

New visitor, homepage

Cold-start from catalog attributes

First-time visitors get a homepage ranked by catalog affinity and popularity signals from similar sessions. Within two pageviews, session intent starts refining the ranking.

Result: new visitors see relevant products from session 1
Returning shopper, PDP

Related items ranked by session intent

A shopper browsing home textiles gets PDP recommendations that match their session affinity, not a generic "customers also viewed" based on purchase co-occurrence.

Result: higher PDP engagement and cross-category discovery
Active cart, cross-sell

Complementary items, not random upsell

Cart cross-sell slots surface products that complement what's already in the cart and match the session's category signals. Not "you might also like" noise.

Result: cart value increase without promotion dependency
Post-search filter

Search results ranked by session, not just query

A search for "blanket" on a home goods site has a different intent profile per shopper. Gift buyer vs. home decor buyer gets a different ranking of the same result set.

Result: higher search-to-cart conversion from the same queries
Integrations

Works with your existing stack

No replatforming. Aislegleam plugs into your current commerce platform via a lightweight tag or API.

Shopify
WooCommerce
BigCommerce
Magento
Custom API

See the platform in detail

Walk through how the data pipeline, intent modeling, and surface delivery actually work.

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