We are problem solvers

At AI Satellitez we believe it’s not the data that solves real world problems but it’s data together with critical thinking, analytical reasoning and domain knowledge.


Our engineers and data scientists use the best technologies available on the market, and we are constantly adding new ones through training, research and additional publications.


Our system engineers and data scientists will work closely with your team to create a big data processing system customized for your business requirements and your products.

Our mission is to provide artificial intelligence and machine learning models to businesses to optimize their operation. It is our core value that we are problem-solvers and build data driven business solution for our clients. We are a big data machine learning and artificial intelligence organizations providing end to end modern modeling solutions. We provide the foundation for a complete artificial intelligence transformation of your everyday business, digitization, securely enabling the Internet of Things, and bolstering human capability in the decade of intelligence through machine learning. Now is the time.
With our exceptional and broad problem solving minds and understanding of data, we create the opportunity to fully unleash the power of the artificial intelligence. We are focused on delivering data results and insights as quick and efficient as possible to support the growth of your business. That is our progressive homework and we have A+ from our previous clients.



Our client is a major telecom organization with revolutionary products, a proven process, and 30 years of delivering hosted technology solutions to complex business problems in US.

Considering our client’s use of classical models to identify fraudulent calls, their customers were still losing over 650 million dollars. At Satellitez, we built a learning algorithm (supervised, unsupervised, autoencoders) to identify fraud, 1 in 100,000 with over 80% catch rate and false positives below 1%. Our team of data scientists used deep neural nets machine learning with Amazon engines, Tensorflow and H2O autoencoder to perform the necessary experiments. At the end, the model was integrated into their cloud environment.


Our client is a state-owned bank with the objective to select potential customers for installment loans using machine learning algorithms to maximize revenue.

Prior to Satellitez modeling, our client had no system to identify customers with a potential of repaying the loan immediately after a large credit card activity. The main point of the project was to identify the right subset of transactors, so our client could make them a lower rate offer to avoid bouncing. Of course, this should be the case without making an offer to the revolvers segment.

Our data scientists used learning algorithms with clustering, correlation analysis, classification and time series modeling. The essential reproducible analysis contained the characteristics of sample customers, shopping categories identification, Box-Cox normalization, payoff period and programming in R and Python. The model was deployed on local VMs.


Our client was a major financial institute who had a fixed method to offer credit line increase to customers without the ability to change it based on customer’s current or past behavior.

The major contribution of our team in this project was the use of random forest and general linear model algorithms to select the right subset of customers. At Satellitez, we were able to increase the rate of success from 50% to 72% and then to 86%.

The steps of reproducible computation in Sagemaker included, training, validation, testing and usage of utilization compatible to our client’s standards and their methodology. Twelve different datasets from different sources containing multiple records for over 3 billions were available which required data integration and data reduction which was deployed on cloud.


Our client was a major tech organization with extremely powerful products, a proven process, and 50 years of delivering technology solutions to businesses in US.

Our client’s DevOps were receiving just over 200 alerts per minute with almost 90% noises. The use of operating human force to identify system alerts and to open tickets associated to each major outage was beyond inefficient. Our team of data scientists developed an unsupervised model with just three experiments to fit a model which can look at the changes of patterns which are under development to be an outage later and to predict them successfully.

This proactive modeling gives a competitive edge to our client over their competition to take proper action in minutes where things matter the most.

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