What we have done

  • Statkraft needed help to operationalize a prototype for distributed hydrological forecasting, and hired one of our consultants to help. We ended up reimplementing the whole system, using modern templated C++, and equipping it with a Python interface. The new system, called Shyft, runs several hundred times faster than the old prototype, and is currently used both operationally at Statkraft and as a research tool by hydrologists in academia.  Shyft is open source, and can be found at https://github.com/statkraft/shyft and http://shyft.readthedocs.io/en/latest/.
  • Long term power production planning involves resolving a comprehensive market model with power consumers, producers, and transmission lines, down to the individual reservoirs and hydro power stations in coupled water courses. This is done by running simulation software that outputs a fairly large amount of data in the range of 15 - 20 GB each time a simulation is run. We have helped building an analysis platform for this problem that contains a domain model in which simulation results are inserted after they are computed. Furthermore, we have developed advanced techniques for retrieving and visualizing the results in terms of time series data and statistics, along with advanced aggregation of results to allow forecasts to be viewed in ways earlier not feasible.
  • A customer wanted to optimise their allocation of man-power relative to demand. We have developed and implemented a full system from demand forecasting based on historical data, to optimised work schedules (based on Google optimization tools), and implementation in their time-management system.
  • A small bioinformatics software company is trying to disrupt in precision cancer immunotherapy. They marked themselves using machine learning to predict the effect of a therapy. One of our consultant went in and took the role developing the backbone of the scientific platform implementing state-of-the-art machine learning applied on observational protein data scaling on the range of 10-20 GB. The algorithms including support vector machine, random forest, artificial neural network, and topic modeling. Some of these machine learning implementation are currently (as of May 2018) marked leading within their applications.
  • Mobile payments  -  Since 2013, we have been involved in projects with Norwegian companies building mobile payment systems; mCash, Auka, and Vipps. Our involvement have mostly been on the backend, working on core transaction and settlement logic as well as internal and external APIs. When working on payment systems, the concepts of data consistency, idempotent operations, and fault-tolerant operations need to be well understood. In order for the system to perform well under load, it is required to understand the relevant performance characteristics of the system including e.g. modern cloud infrastructures. Through the years, we have built domain knowledge within the field of mobile payments that we now can apply with existing and new customers working on problems in the financial sector. The technologies we have applied in this field are mainly Python and Go, deployed on the Google App Engine and Azure stacks. Both relational and document database models have been used.