Be it guidance in using AI to reach your business goals, or support in implementing a new concept you have, you can rely on us to support you, no matter the needs.
We design the data architecture, algorithms and strategic work of projects ourselves, which empowers us to deliver quick and cost-efficient projects for our clients.
Our team is composed of the sharpest software designers, mathematicians and algorithm designers in the industry. Many of our employees have more than 15 years of experience in demanding customer projects and development and use of AI.
We process your data securely and use the Azure cloud environment. Our servers typically reside in the EU and Norway. We own the IPRs of our algorithms and development is entirely in our own hands. This way, we can easily tailor our optimisation solutions to meet the individual needs of our clients.
Route optimisation is the process of finding the best route for a delivery driver to take when delivering multiple packages to many locations. This doesn’t necessarily mean finding the shortest route, but the most efficient one.
The Packing Planner is a specialised tool that calculates and optimises order quantities based on the actual demand situation (customer orders, latest sales forecast). This calculation is not based on static safety stocks, but checks the range of coverage and considers the actual demand situation.
Packing optimisation makes the packing process more economical and efficient to the supply chain. Product retailers seek package optimisation to help them pick the right kind, size, and quantities of packing material in order to achieve cost savings throughout the supply chain.
The goal of personnel optimisation is to minimise the number of people at work at any given time. If the amount of workers needed can be estimated ahead of time, substantial savings can be achieved. A machine learning algorithm can predict the need for workers in a restaurant, for example. This prediction is based on things like the current time, weekday and time of year.
The goal of material planning is to minimise waste in production. This can be done by optimising the way raw material is used in the production line. Machine learning can be used to automate and improve these processes.
Composition planning figures out the best possible composition for a material in order to achieve the wanted properties. These properties might be something like durability, elasticity or cost.
Time series analysis refers to a particular collection of specialised regression methods that illustrate trends in the data. It involves a complex process that incorporates information from past observations and past errors in those observations into the estimation of predicted values.
Image processing techniques as well as digital image capture equipment provide an opportunity for fast detection and diagnosis of quality problems in manufacturing environments compared to traditional dimensional measurement techniques. The image processing is used to detect quality faults in real-time in order to guide manufacturing processes.
Speech recognition is an interdisciplinary subfield of computer science and computational linguistics that develops methodologies and technologies that enable the recognition and translation of spoken language into text by computers with the main benefit of searchability.