We believe that it is not data size or volume that is most important for organizations. Instead, it's the knowledge extracted from this data. We can help you get truly important insights from your information assets: no matter their size.
Examples of predictive analytics
Our data scientists work together closely with industrial experts on development of applications that enable the realization of the Industry Revolution using IoT. Get inspired by our first scientific application adapted for Industry 4.0.
Management and prediction of energy consumption
- Move from basic monitoring of energy (usage) to prediction, optimization and adaptive management
- Get more out of your smart measurement devices: monitor your devices' operations without further auxiliary sensors
- Avoid critical failures and production downtime thanks to timely warnings in the event that a device is not working properly
- Our algorithms learn automatically from your data and alert you provided your devices slip into an anomalous state
- Thanks to a hybrid solution, we can incorporate further parameters and fine-tune the automatic system to your environment
- Save maintenance costs and time thanks to smart planning and provision of relevant information
- Optimize how you work with replacement parts: stop excessive warehouse inventory levels of replacement parts while avoiding later orders
Artificial Intelligence in industrial analytics
There is hardly a faster developing field than Artificial Intelligence. Keeping up with the latest research is a full-time job: it's our job.
- Full-stack statistical analysis: descriptive, exploratory,
inferential statistics. Generating hypotheses and their validation.
- Ad hoc analyses, quantitative methods for finding causes
- Machine learning: from linear methods to deep neural networks
- Uni- and multivariate predictive models for time series
- Unsupervised learning: discover the hidden structures in unlabeled data
- Stacking and boosting models
Optimal solution based on customer needs
We always choose the best tool:
- Best of open-source analytical algorithms improved and fine-tuned to your particular needs.
- Python analyses / containers for machine learning (numpy, scipy, mat plotlib, pandas, scikit-learn, statsmodel and many more)
- Artificial intelligence using deep neural networks: Tensorflow from Google
- Support for enterprise Big Data applications (Hadoop ecosystem: HFDS/Hive/HBase, etc.)
- Streaming Big Data Analytics stack: Kafka, Spark, Flink
- R libraries for data analysis and machine learning (tidyverse, caret, predictions, time series, xts, qunatmod and many more)
What makes our solution different
We are bringing together the worlds of industry and analytics. We combine expertise from many industrial sectors and our analytics skills to create cutting edge analytics applications tailored to you need. We are not doing scientific research, rather we are solving real world problems. Real world data is often chaotic, flawed or incomplete. However, with an integrated approach and by selecting the optimal technologies we can help you to finally get deep insights into your business.
- Data cleaning, assessment and formatting: Data conversion to the right shape
- Robust models: We can work with incomplete data, non-standard data, and formatting errors
- Access to predictions and results through a responsive, interactive Business Intelligence manager application