Engineering & Scale

Personalization & Recommendation Engines

We build recommendation systems and adaptive digital experiences that show users the right product, content, action, or message at the right time.

Personalized product, content, and workflow experiences powered by behavioral and contextual data.

What it is

Personalization is the layer that makes a digital experience feel relevant to each user.

Recommendation engines use behavioral, contextual, and product data to suggest what a user is most likely to want next.

This offering is strongest for ecommerce, SaaS, marketplaces, content platforms, learning platforms, media, and apps with meaningful user behavior data.

We build ranking, segmentation, personalization, and experimentation loops that can be measured against conversion, engagement, retention, and revenue metrics.

What we build

Recommendation engines

Product, content, next-best-action, search, and feed recommendation systems.

Adaptive journeys

Personalized onboarding, email, notification, and in-product experiences.

Segmentation and ranking

Models that decide what to show, when to show it, and to whom.

Experimentation loops

A/B testing and optimization workflows tied to measurable product outcomes.

Common use cases

Recommend products based on browsing and purchase behavior.
Personalize search results for each user.
Suggest the next best action inside a SaaS workflow.
Create a personalized "For You" feed.
Recommend help articles based on user behavior and account context.

Delivery process

01Map the user journey and business goal.
02Audit behavioral, product, and content data.
03Choose the right recommendation approach.
04Build ranking, personalization, and serving logic.
05Test impact through experiments and conversion metrics.

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Related capabilities

AI Systems & Automation