RASPLEX

Rasplex converts any enterprise data into valuable predictions through the deep learning process using neural networks.

Red or Blue Pill?

Intelligence (red) offers harsh knowledge and the brutal truths of reality while the lack of of it (blue) offers a life filled with the blissful ignorance of illusion.

Rasplex (the red pill) is a Machine learning engine that predicts data behavior through the deep learning process. More specifically, Rasplex predicts customer behavioral trends by giving insights into who will stop using your product or service, when and why.

LEARN MORE

Integration with Rasplex


STEP 1 - CONNECT YOUR DATA

Rasplex helps Enterprises capture customers' data from multiple
sources and streamlines information into one secure centralized
platform.

Data is processed through the business rules of how the
application should flow to seamlessly transfer selected data
fields into the Rasplex Platform in accumulative read-only
formats.

STEP 2 - ACTIVATE DEEP LEARNING

Rasplex proprietary algorithms provide real-time business
intelligence to users at the click of a button to analyze
variables, explore and foresee future trends.

Rasplex helps Enterprises predict customer behavior through the
Deep Learning process enabling foresight into how long
customers will stay with your product or service and provide
corrective actions.

STEP 1 - INTERACTIONS.

Everytime a user interacts with your business their information is collected and transferred into the Neural Network.

Predictions with Rasplex

Converting data into valuable predictions through the Deep Learning using Neural Networks.

STEP 2 - DEEP LEARNING.

The Neural Network passes that information through multiple classifiers to better understand the hidden layers that make your data behaves the way it does.

STEP 3 - PREDICTIONS.

The Neural Networks algorithm predicts Customer lifetime by giving you projections of how long customers will stay with your product or service.

QUESTIONS RASPLEX CAN ANSWER

1. X amount of your customers will leave on Y day

2. These Y customers have these X similarities

3. These Y customers will leave because of such X reasons

4. You can do X & Y to reduce the chances of them leaving

5. X amount of customers will be using Y service for X time

6. Keep doing X to keep these customers around for Y years

LEARN MORE