How to create a personalized experience for every user

Alie is an AI-powered recommender as a service for businesses that want to create a personalized user experience for their customers. Alie’s AI is powered by algorithms that are designed to anticipate your customer's needs in real-time and create personalized experiences.

Alie ingests item data, user data & behavioral data from your website or app to provide real-time recommendations. Implementing Alie for your website/app is fairly easy and can be achieved in four simple steps.

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Learn how simple it is to set up recommendations for your website/app!

Sign Up & Initial Setup (7)

Go to the Alie Sign Up page and enter the required information, and click on the Next button. Your free trial account will be created and you will be navigated to the CMS portal.

Alie is built using an API-first approach, allowing seamless integration with your website & apps.

Alie offers a two-way integration process - You send the data to Alie in the first step and read data from Alie in the second step.
Data Acquisition

There are three ways with which Alie can collect data. You can use APIs, CSVs or RSS feeds to provide item and user data. You can use Webhooks & JS Plugin or use Cookies for sending user activity or behavioral data.

User and product data can be uploaded with simple CSV files, RSS feeds or APIs. You can also push all meta-information associated with your item & user data. More meta information you provide better it is for system.

Behavioral data is gathered either explicitly - reviews, ratings, likes, etc. or implicitly - gathered from available data streams, such as search history, clicks, order history, and other activities.
Generating Recommendations

Data is acquired using Fast Data Acquisition method & stored in high performance database

Choose an appropriate algorithm from a drop-down menu on Alie CMS. Alie analyzes the user, item, and behavioral data based on a chosen algorithm to generate the recommendations.

Final Recommendations can be filtered using standard filtering methods provided in Alie CMS.
Data Acquisition

Alie provides recommendations data to your website and apps using accelerated inference APIs.

APIs return a list of Recommended Product(Item) IDs for a user based on the model that is trained.

You can then use these Product(Item) IDs to hook recommendations on your website or app to create a unique personalized experience. Recommendations can be shown on home pages, product pages or shopping carts, etc.
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All set! Your website/app is ready with recommendations now. Your audience can now experience a personalized browsing experience. In case you require any further assistance, you can always write to us at sales@muvi.com or contact us by filling up this form. We’re here to assist you.
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Frequently asked questions

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Yes. We are committed to improving our product and customer experience. We maintain a priority list of new features & UI upgrades based on customer requests. Our team is quite strong in research, innovations, and faster implementations. You can check our latest list of features here.

By default, content-based models are tuned to reduce the cold start issues. Alie uses the End-User Interaction algorithm where Alie starts showing similar item recommendations even if there are very few (or just one) user-item interactions.

Yes. Definitely. 

Alie uses browser cookies to capture user activity & product data to generate relevant recommendations, for guest or anonymous users!

Our pricing is purely usage-based or pay-as-go - you will never have to pay more than you use. We have flat pricing of $ 0.0005 per API request, whereas the minimum monthly charge would be $99. Please check our pricing page for more details.

Muvi's core features include built-in CDN for content delivery, DRM for content protection, multiple monetization models, an online video player, and automatic encoding and transcoding. Muvi offers 100+ features designed to deliver a seamless streaming experience. Sign-up for our Free Trial Now!

We use collaborative filtering (matrix factorization, nearest k, supervised learning …) algorithms, which work on the interactions (views, purchases…), and content-based algorithms, which work on the item metadata (titles, descriptions…). You can get more info here

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