AI Merchandising for DTC Brands

Show every shopper the catalog they actually want

Aislegleam personalizes homepage, PDP, and search for each visitor. The merchandising judgment of an expert buyer, running across your full catalog in real time.

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Used by DTC teams running 5K-100K SKU catalogs

Doveridge Goods Solane & Co Farrow Supply Crestholm Living

Linen Throw
Blanket

$64

Ceramic Pour
Kettle

$89

Walnut Tray
Set

$48

Cotton Napkin
Bundle

$36

Early-access pilot partners

Doveridge Goods Solane & Co Farrow Supply Crestholm Living Merritt Home

The merchandising problem

A skilled merchandiser can actively manage around 200 SKUs at a time. The other 39,800 in your catalog are deciding their own fate.

Manual merchandising made sense when catalogs had 200 products. At 5,000 or 40,000, you're working around a structural gap in your stack. Every week, the homepage carousel gets curated by hand while most of your catalog goes unseen by the shoppers who would actually buy it.

How Aislegleam solves this

200

SKUs an expert merchandiser can actively manage by hand per week

39,800

Products in a typical 40K-SKU catalog that never get seen by the right shopper

62%

Of catalog revenue potential sits in the long tail that manual rules never reach

How it works

Three steps from catalog to personalized surface

Connect catalog and clickstream

Aislegleam ingests your product catalog via feed and collects behavioral signals through a lightweight tag. No data team required. Shopify, WooCommerce, BigCommerce, and custom APIs all supported.

Learn each shopper's intent in session

Our models process what each visitor views, searches, and adds to cart in real time. No login required. Anonymous session signals are enough to build a strong intent model within the first two page views.

Deliver personalized surfaces per session

Ranked product sets are delivered to your homepage, PDP, search results, and cart via JavaScript widget, REST API, or Shopify Liquid tag. Under 200ms p99 latency.

Platform capabilities

Every surface your catalog touches

Recommendation Engine

Real-time ranked product sets for homepage carousels, PDP related items, and cart cross-sell. Combines collaborative filtering with session-based intent signals and catalog affinity scoring. Works from session 1 without purchase history.

+22% add-to-cart across beta cohort

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Search Personalization

Re-rank search results per session using the same intent model. Shoppers who exhibit category affinity see those products surfaced first.

Explore search →

Merchandising Rules

Override ML ranking with editorial rules for seasonal pushes, inventory clearance, or brand partnerships. Rules layer on top of personalization, not against it.

Explore rules →

A/B Testing and Attribution

Built-in A/B framework measures what the personalization actually lifts. Revenue per session, add-to-cart rate, and catalog depth per cohort.

Beta cohort results

What pilot partners are seeing

+22%

Add-to-cart rate lift

Across beta cohort, 90-day windows, internal attribution

+18%

Revenue per session

Across 8 pilot partners, internal A/B measurement

Pilot partners, 90-day test windows

Beta-access cohort, all DTC brands 5K-100K SKU range

What pilot partners say

From the merchants who've run it

"We had tried manual A/B testing on homepage carousels for two years. Aislegleam's session-based model picked up intent signals we couldn't segment by hand. The 22% add-to-cart lift was the first clean number we'd seen in three years of trying."

Jordan M.

Head of E-Commerce, Doveridge Goods (early-access cohort)

"We're an apparel brand with 8,000 SKUs and a lot of seasonal variation. The cold-start handling was what surprised us most. New arrivals were getting recommended to the right shoppers within 48 hours of catalog sync, before we'd built any purchase history on them."

R. Abrams

Founder, Solane & Co (8K SKU apparel, early-access cohort)

Pricing