Custom software
for data and analytics

Define the problem and write or refine the technical brief

Choose technologies, assemble the team and estimate effort and cost

Agree on a delivery plan, build the software and deploy it

Explore business goals

Business goals

Evaluate campaign performance

Identify customers at risk of leaving

Forecast demand

Recommend relevant products to each customer

How we develop your system

Review your workflows and IT infrastructure

Identify tasks to automate and select the technology

Write technical documentation and document applicable requirements

Build and integrate the software

Maintain and support the system after launch

What you get

An agreed technical specification

A choice of tools suited to the task

A work plan and cost estimate

Software integrated with your systems

Support after deployment

Project results

75% average reduction in store inventory

Objective:

Reduce inventory while keeping products available in stores.

In 97% of observations, actual sales fell within the forecast range

Solution:

The model forecasts a range of weekly sales for each product in each store, up to a year ahead.

Tools: R, Python

Algorithms: XGBoost, CART, k-means

10% maximum difference
from the expert forecast

Objective:

Automate financial planning at head office by forecasting regional spending.

Over 120 cases of fraud
detected in the regions

Solution:

We integrated a tool built with KXEN and SAP BI into the client’s systems. It uses historical spending and regional economic indicators to forecast costs, detect fraud and adjust the budget.

Tools: R, KXEN, SAP BI

Algorithms: KXEN time-series algorithms

At least 9 subscriber segments for targeted campaigns

Objective:

Group customers for targeted campaigns and identify subscribers who also use competing providers.

86% accuracy in identifying subscribers who also used a competitor’s services

Solution:

The system groups subscribers and describes each segment. It estimates how likely each subscriber is to use another provider.

Tools: R, Python, Spark, Hadoop, Hive

Algorithms: XGBoost, CART, k-means, k-medoids, t-SNE

8 data sources connected

Objective:

Identify and prevent product losses and detect declines in the quality of petroleum products.

Over 1,000 incidents detected

Solution:

We connected the data sources to a Hadoop platform and set up processes to extract, transform and load the data. We built analysis modules and a web application for analysts.

Tools: Python, JavaScript, Shell, SQL, Hadoop, HDFS, Spark, YARN, Hive, Tez, Hue, ZooKeeper, Luigi, Kafka, NiFi, PostgreSQL, Microsoft SQL Server, Oracle, PI System, Django, AngularJS, Bootstrap, CSS, Docker, CentOS/RHEL