AI & Data · Solution we build

Recommendation Engine

Personalised product and content recommendations that lift engagement. A recommendation engine uses behavioural data and machine learning to suggest the right products, courses, videos or articles to each user, increasing engagement, average order value and retention.

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Recommendation Engine: Personalised product and content recommendations that lift engagement.

Personalised feeds Similar & bought-together 10–16 weeks typical timeline

Typical timeline10–16 weeksfor a first production release

PlatformsAPI for web and mobile apps

IndustryAI & DataeCommerce stores, OTT and media platforms

Key features

What your Recommendation Engine includes

Personalised feeds

'Recommended for you' based on behaviour and profile.

Similar & bought-together

Item-to-item recommendations on product pages.

Real-time signals

Recommendations adapt during the session.

Cold-start handling

Content-based suggestions for new users and items.

A/B testing

Compare algorithms and measure uplift.

Easy integration

REST APIs and SDKs for web and apps.

Who it's for

eCommerce stores
OTT and media platforms
EdTech platforms
Marketplaces
Higher conversion and order value
Longer user sessions
Better content discovery
Measurable uplift through A/B tests

Business benefits

Higher conversion and order value
Longer user sessions
Better content discovery
Measurable uplift through A/B tests

Technology stack

Built on proven, scalable tech

Typical stack shown. We choose technologies based on your scale, team and existing systems.

PythonTensorFlow / PyTorchFeature storeRedisAWS SageMaker

How we deliver

From discovery to launch

STEP 01

Discovery

Workshops to understand your workflows, users and must-have features.

STEP 02

Design

Wireframes, clickable prototypes and a technical architecture you approve.

STEP 03

Build & test

Agile sprints with demos, code reviews and automated QA.

STEP 04

Launch & support

Cloud deployment, training, monitoring and ongoing improvements.

FAQ

Frequently asked questions

How long does it take to build a recommendation engine?

A typical first version takes around 10–16 weeks, depending on the features, integrations and platforms you need. We share a detailed timeline after the discovery phase.

Can the features be customised for our business?

Yes. Every solution is built for your workflows. The features on this page are a starting point; we add, remove or adapt modules based on your requirements.

How much data do we need?

Meaningful personalisation usually needs a few months of user interaction data; we start with simpler rules and content-based models when data is limited.

Who owns the source code?

You do. On project completion the source code and intellectual property are handed over to you, and we can continue with support and enhancements if you wish.