Services

Veltronix was founded as a data engineering company in 2013. The data-intensive projects our team has delivered include:

• Production and trading optimization software for a combined cycle gas power plant
• Budget allocation and bid optimization algorithms for an online advertising platform
• AI-based content recommendation system for a large pay-TV operator

Our data engineering and science services include the full lifecycle of a data product - from server provisioning to creation of reports and dashboards and machine learning model development.

• Data Ingestion & ETL: A robust data ingestion pipeline is crucial for the success of a data project. Whether the source of data is RDMBSs, IoT sensors or outside APIs, the ingestion process should work reliably with high uptime and low latency.
• Data Quality Control: One essential component of any dependable data ingestion pipeline is the quality control unit. Even the best ML algorithms or reports will not create value if the input data has errors, corruptions or drift. For the best results, we go beyond simple count or sum checks and utilize tools like Amazon Deequ to continuously monitor for anomalies and inconsistencies in the ingested data.
• Data Modeling: Large organizations utilize data from many different source systems and business domains. Data Modeling helps us create and present a coherent picture to be used in analysis, report generation and ML model development.
• Real-time Reporting: Being able to observe the business operate in real time presents managers with unprecedented opportunities for accelerated decision making and improved customer service. We use stream processing technologies like Apache Flink and Spark Streaming to create dashboards thet update in real time enabling our clients continuously monitor and optimize their business processes.
• Turnkey Delivery of Big Data Systems: We work with providers like Cloudera and AWS to create custom Big Data infrastructure for our clients, designed specifically for their individual needs.
• Machine Learning Operations (MLOps): Designing processes and utilizing automated systems to manage the lifecycle of your machine learning models is essential for efficient, scalable and low-risk use of AI technology. We have worked with large enterprises to develop MLOps capabilities to reliably take machine learning models to production, validate and continuously monitor their performance.