E-commerce & DTC Brands

Higher AOV on every session without adding ad spend

We build a personalised recommendation engine from your Shopify browsing and purchase data. Embedded on the product page, cart, post-purchase, and in email. Your customers see what is relevant to them.

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The problem

Sound familiar?

Your recommendations show the same bestsellers to everyone

Bestseller lists are not recommendations. A customer who already bought your flagship product sees it again. A high-LTV repeat customer sees the same homepage as a first-time visitor.

Third-party recommendation widgets share your data with competitors

Most plug-and-play tools train on a shared model. Your customer behavior teaches a system that also serves the brand selling against you.

Post-purchase upsell is an afterthought

The moment right after a purchase has the highest intent of the entire session. Most brands show a static discount code. A recommendation engine turns it into a second sale.

Email recommendations are static blocks

You send the same featured products in every email. A system that inserts personalised recommendations per recipient can double email-attributed revenue without changing send frequency.

The solution

What we actually do

We build a recommendation engine trained on your Shopify purchase history, browse events, and product metadata. Personalised widgets on PDP, cart, and post-purchase. Dynamic recommendations in Klaviyo emails. You own the model and the data.

What you get

What's included

Shopify behavioral event pipeline — browse, add-to-cart, purchase signals

Recommendation model trained on your catalog and customer history

PDP widget: complementary and frequently-bought-together products

Cart widget: upsell and cross-sell recommendations at checkout

Post-purchase page: next best purchase recommendation

Klaviyo integration: dynamic product blocks personalised per recipient

Performance dashboard tracking recommendation CTR and attributed revenue

The process

How it works

Data

We audit your Shopify event data and catalog metadata to define the training signals.

Model

We train a recommendation model on your purchase history and behavioral data.

Embed

We deploy recommendation widgets to PDP, cart, and post-purchase pages.

Measure

We track CTR, AOV lift, and email-attributed revenue and report monthly.

Proof it works

The Meatery

AI Voice CRM, Shopify multi-tenant

Read the case study

The offer

From $3,000/mo

Initial model build included. Retraining and widget updates ongoing.

Common questions

Frequently asked

01How much data do we need to start?

02Does it work with Headless Shopify?

03Will it conflict with our current Shopify theme apps?

04How is this different from Shopify's built-in recommendations?

05Is TechEmulsion based offshore?

Ready to get started?

Let's build your ai product recommendations system

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