What we do

Control the power of data, enhance operational efficiency and make informed decisions that drive growth, sustainability and competitiveness

Collect, analyze and interpret data using different ML techniques

Create, deploy and industrial-scale usage of predictive models

Further industrial applications such as process optimization, quality control and safety enhancement

The Value of INSUS

Use AI/ML to reduce environmental impact

ML techniques are revolutionizing sustainable manufacturing by optimizing processes, predicting maintenance needs, minimizing waste and enhancing resource efficiency.

Data Science & AI

With the help of our specialized resources such as Data Scientist, Data Engineer, ML-Ops, ML Engineers, Software and DevOPs IT Automation Engineers you can benefit with the following solution offerings:

Business problem or opportunity identification

Identify the potential opportunity to address a case study/business problem using ML techniques and set clear expectations regarding the desired outcome, type of data and data quality.

Data collection and preparation

Gathering of data and preparation from different sources such as machines, sensors and databases. This is a critical step, as the quality of the collected data have an impact on the performance of the ML model.

Data analysis using ML techniques

Analysis of data using ML techniques to identify patterns, trends and relationships. The data is required to be cleaned, pre-processed and formatted so it can be used by the ML algorithms.

Model design

Model design including developing of complex models that can be used to make predictions.

Model Training and Evaluation

First, train the ML model involves feeding the model with prepared data and allowing it to learn from it. Second, evaluate the ML model involves assessing the performance of the model.

Model Deployment

Once performance of the model is accepted, then is ready to deploy it which means make available to users so that they can use it (E.g. to make predictions). Integration with existing applications is expected in this step (E.g. with software, web browser, API, etc).

Model industrialization

Once model is successfully deployed, it is ready to scale up which involves monitoring, maintaining and updating the model. It is key to evaluate user adoption to ensure the model is working as expected and it is solving the business problem identified in the first step.

Model design and deployment

We deliver tailored solutions, developing of complex models that can be used to make predictions including integration of models with existing systems

Management of resources

Ultimately, the focus is on improving the management of resources like reduction of waste or improvement of energy efficiency, otherwise known as sustainable manufacturing.

Languages, Tools, Software

  • Artificial Intelligence (AI), Machine Learning (ML) & Deep Learning and Computer vision
  • Reporting & Data visualization: Data Studio, PowerBI, Qlik, Tableau
  • Programming Languages: Python, Java, Tensorflow, Scala, Spark, PyTorch
  • Website scripts: XML, JavaScript, JSON and SQL/NoSQL databases
  • Cloud platforms: AWS, Azure, GCP, AI and ETL development, Imagine Aerial (drone photography and video).

Our Most Recent Projects

Here are some of our developments and success stories where Insus made a change. Let us provide you with a tailor-made solution with a high degree of knowledge and experience.

Looking for more solutions?

IIoT and Industrial Automation

Project & Business Process Management

Let’s get started

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