Featrix Feeds simplifies personalization and recommendation for all apps

Simply send events and get predictions

No matter what type of application you're building, whether it's internal to a business or a consumer iPhone app, customers expect smarter applications. Featrix Feeds is a easy way to provide predictive recommendations to your customers no matter the app type with almost no work for you.

Featrix Feeds gives you a robust and easy recommendation system to enable personalization in your apps and web services without a big investment.

Simply send event streams to Featrix with a single API call and ask Featrix to provide recommendations with another API call. You can change your data formats, provide positive and negative events, and even put change event weights so that Featrix can help you achieve your business goals without the complexity of a big machine learning project.

Now you can implement AI successfully
with the data you actually have,
not the data you wish you had.

Many data experts insist that enterprise data is in no shape to be used with machine learning due to problematic data. For traditional approaches to machine learning, they are absolutely right. Data preparation is a considerable burden and an ongoing challenge for machine learning project prototypes and production deployments. That's one of the reasons we built Featrix. Once you try machine learning with Featrix, you'll never go back to the old way of spending days or weeks messing with data.

Enrich data without joining

The best way to improve model accuracy is to enrich the data, i.e., to add more data to the model that provides additional context. This usually involves doing some kind of join, which is computationally expensive, and when the data requires mapping multiple fields together that don't quite line up, it can turn into a week long project.

Featrix lets you skip over that by training an embedding space with multiple data sources. You can bring together disparate data and create models across the data sets, uncovering insights that are not directly in any single data set.

The power of embedding-based analytics lets us build this capability without the join--not only do you not do the join, but we don't either. This is a huge savings of your time and compute bill of traditional data providers.

Pretraining & Fine-Tuning

Do you have strong opinions? You're in luck. Featrix offers tons of flexibility, but you don't have to use it. You can also fine-tune the Featrix embedding models for your own specific needs, as well as train models on a subset of the embedding space.

Controlled spend

No one wants a surprise bill. You can set a budget when creating your machine learning models and ensure your training never exceeds that budget. If you want more accuracy, simply apply more budget to the model and Featrix will pick up where it left off before.

With Featrix, your bill is always predictable and never exceeds what you configure.

Multi-modality enrichment

Featrix's underlying use of vectors enables you to combine embeddings from other systems into one unifying representation. You can connect other embeddings, use standard open source models, provide your own, and more. Featrix handles all the versioning, semantics, and management of the vector space.