Our AI Solutions
We enable businesses growth through efficient, reliable and easy to implement AI solutions.
The AI Potential
How can you make sense of the new global reality and chart ideal paths toward a better future?
As a foundational technology, artificial intelligence has the potential to boost significantly the business advancement in every industry with its wide range of benefits. AI and data-driven decision making are reshaping the way companies do business. Data is the lifeblood of AI.
However, although many companies are collecting and storing data, very few know how to analyze it in order to tease out the hidden truths that lead to insight and change. AI is the engine that drives this innovation.
Bringing AI initiatives to life is sometimes painful.
More than 85% of AI projects never reach production or create value.
We recognise these challenges and apply a clear, robust and easy to implement process.
In addition we make sure that there is a good balance between business and technology expertise in each project we deliver. Thus we can guarantee a fast and transparent process so that business can grow to its full potential.
Our delivery process is divided into 5 distinct steps:
- Discovery: Reviewing the current capabilities and defining future goals to make recommendations for tools, technology, and architecture;
- Business Analysis: Analyse the current business processes and produce GAP Analysis. Agree steps for developing and deploying the AI solutions;
- Proof of Concept: Test a small-scale system, proving the viability of ML models for your problem;
- Implementation: MVP - implement the solution in the business process and monitor the quality and agreed KPIs;
- Improvement: Continuous improvement of built models to raise the quality of insights and to keep up with the changing environment.
From day one, I felt not in the role of pushing but in the role of being pulled in executing the project.
Transforming Manufacturing with AI
The machine learning (ML) field has deeply impacted the manufacturing industry during the last decade. The application of smart sensors, devices, and machines, to enable smart factories means a lot more data is collected and available for analysis. ML algorithms have various applications in production plants:
- Quality control - The use of sensors, together with machine learning, allows continuous evaluation of the quality in each of the production phases
- Preservation of knowledge – some processes are very specific and require sometimes years of experience like in the cement factories. Through ML this knowledge is accumulating over time and can be used both in training programs and applied in the production process
- Predictive Maintenance - the ultimate goal of a manufacturing plant is to ensure maximum production uptime within a defined budget. To maintain the production machines within optimum operational parameters at all times, two main activities are always ongoing - planned maintenance and unplanned repairs. The common maintenance practices are “reactive” - incapable of predicting and avoiding eventual failures With the use of ML analyses of the operation can be done in advance, allowing taking proactive actions to avoid machine failure, and eventually preventing the expensive production stops (as high as 5-digit numbers in USD per hour). A predictive maintenance solution can use the measurement from various operational parameters like electrical current, vibration, temperature, sound, etc, to forecast malfunctions and provide real-time monitoring of the equipment.
Please have a look at a case study from a project we did with a major manufacturer of tanks, vessels, heat exchangers, and bulk handling systems to industry-leading companies such as ADM, Quarzwerke, Huvepharma