We use pre-trained models where possible to immediately benefit from the opportunities of AI. Creating conversational interfaces like chatbots, making videos searchable, classifying images, converting audio to text, extracting information and sentiment from text, and translating text are all easy. For other, more specific data needs, we use Keras and Google's Tensorflow to generate tailored models.

Angular Demo

Most models run at the back end and are consumed via an API.
Some models are better off running at the front end. We can help you run models in web apps and hybrid mobile apps - using JavaScript like the image classification we built as a demo.

The three steps of machine learning projects


Large amounts of data is needed for deep learning.
Every organization should have a strategy for collecting data already before planning the next steps.


A neural network is constructed based on the data available, and the output needed. The data is used to train the network. Code created here is only a tool to create and train the network - it is not used in the third step. This may take a while...


But after that, the trained model can be used on many platforms, again and again. It can be deployed as an API at the back end, or as a front end component for web apps and mobile apps. Front end usage enables almost real-time running of the model.

AI technologies will be in almost every new software product by 2020.

Gartner, 2017

Classify, detect, predict, recommend, automate, create

Business benefits for can be found in using machine learning to predict probable values based on new inputs.
This capability to predict probable values for new, unknown input values can be used to classify text or images. A model can detect anomalities in transactions or log events. It can predict future values and recommend new options for the users, based on the previous selections.

Machine learning is a subset of artificial intelligence, AI. Expanding the learning capabilities enables automating tasks. Business logic or decisions can be automated. A feedback loop enables further unsupervised learning.

It also enables creating new ideas. AI-assisted systems can already create new space layout alternatives for buildings, generate images, and create text.


The gap between ambition and execution is large at most companies.
3/4 of executives believe AI will enable their companies to move into new businesses.
Almost 85% believe AI will allow their companies to obtain or sustain a competitive advantage.
But only about 1/5 companies have incorporated AI in some offerings or processes.
Only 1/20 companies have extensively incorporated AI in offerings or processes.
Less than 39% of all companies have an AI strategy in place.

MIT Sloan Management Review, 2017