Human-like AI personalization

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A centralized AI system to automate decision making and intensify personalization across Experience OS.

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Personalize using predictive and deep learning models on web, in emails, and via mobile apps

Replicate the personal touch of in-store shopping digitally

Similar to an in-store salesperson suggesting suitable products for a shopper, the AdaptML™ system mimics human decisioning to present and recommend the most relevant products and offers to each person.

Eliminate blind spots in insight activation and learnings

The system breaks down silos between applications and consolidates data to identify in-the-moment buying intent signals, ensuring learnings are shared and applied across channels.

Lower the barrier to AI adoption and accelerate data-driven decisions

Going head-to-head with decades of human data science experience, AdaptML™ alleviates the need to allocate heavy development resources or build costly in-house algorithms.


Maximize revenue with deep
learning recommendations

Recommend the next best product
series with a self-training
multifaceted predictive model.

Algorithm modeled off the entire product catalog

Upload a product feed with millions of SKUs to power your deep learning-based recommendations.

Rapidly trained & adaptive to changes in user behavior

Self-learning quickly, frequently, and off a huge amount of data, recommendation results are continuously optimized.

Optimal strategy automatically determined

Speed up time to value with an algorithm pre configured based on site trends, user behavior, customer journey, popularity by geo-location, and more.

“We no longer have to manually choose a strategy for our Homepage recommendations, helping us deliver exceptional digital experiences while also saving us time.”
Nadav Yekutiel
Nadav Yekutiel Head of Data,
88% increase in ARPU
68% increase in purchases
Read the full case study →

Predict user-intent in real time
with machine learning

Understand your customers like never before with a neural network algorithm that predicts affinity and future intent with unrivaled accuracy.

Identify one-time and complementary purchases

Affinity profiles and recommendation results update in real-time to account for one-time purchases and subsequent complementary products.

Predict multi-purchase cadences

AffinityML analyzes historical site-wide patterns to intuitively gauge purchase intervals, tailoring user affinities to each individual's unique rhythm.

Enhance affinity profiles with a powerful algorithm

Our affinity neural network algorithm is trained on both the behavior of each individual user and the site activity of all users, enabling it to understand user behavior on both micro and macro levels to deliver precise, predictive, and relevant content.


Recommend visually similar
items with image recognition

Surface attributes that can't be described with keywords to open new possibilities for your catalog and recommendations.

Get customers what they want, faster

Help your customers find exactly what they’re looking for, faster, by recommending visually similar items to the product they’re currently viewing.

Facilitate long-tail discovery

Surface similar, relevant items to the current product regardless of popularity score or metadata tags, broadening discovery across your entire catalog.

Additional capabilities baked into AdaptML TM

Enhance emails with deep learning recommendations

Take your email campaigns to the next level with recommendations predicted to drive click-through, tailored at time of open.

Serve deep learning recommendations in the app

Increase retention, engagement, and session length by recommending products that are anticipated to drive action.

Instantly identify customer intent, even in-session

Beyond past behavior, take into consideration the shopper’s current activity as well as the ever-changing trends seen across the site to refine your recommendations.

Deploy API-based deep learning campaigns

In addition to client-side support, launch your deep learning recommendations and product listing page personalization entirely through the server code for increased flexibility, control, and privacy.

Supports product feeds of all shapes and sizes

Our deep learning recommendation algorithm works with any type of product feed and isn’t dependent or sensitive to the richness of the metadata in your feed.

Deliver on the expectation of personalization

Go from serving additional products that may be of interest with global, contextual, or even affinity-based strategies to predicting items a user is most likely to engage with.

Prove deep learning’s efficacy with robust A/B testing

Compare the deep learning algorithm against any other recommendation strategy to validate your results.

Accelerate the product discovery process and increase sales

From the homepage to within emails and the mobile app, our deep learning algorithm matches consumers at various stages of the funnel with the products they are looking for, faster.

Out-of-the-box recommendation templates

Increase time to market with by selecting from dozens of recommendation templates which are ready-to-be modified and set live using the deep learning strategy.

Understand the business impact of deep learning

Use out-of-the-box testing capabilities to automatically calculate the incremental revenue uplift from deep learning recommendations.

Drive decisions based on actual user behavior

Break free of relying on visual attributes and product metadata to serve similar or complementary items, using real historical and in-session activity to deliver 1:1 recommendations.

Flexible KPI selection

Select either an out-of-the-box KPI or create your own custom metric to optimize towards when experimenting with the different experiences.

Advanced reporting & analytics

Get further insight into deep learning-based experiences by understanding how additional secondary metrics performed.

Custom attribution settings

Determine how results are calculated to align with your business goals with both session- and user-level attribution options.