AI artificial intelligence application project

We will create a strategy and roadmap for integrating AI into your organization. 

Initial consultation

• understanding the challenge
• defining goals
• determination of priorities 
• initial feasibility assessment

Defining the scope

• data and infrastructure evaluation
• exploring solutions
• time and cost estimation
• output: a proposal for the scope of the AI solution 

Submission of proposal

• proposal summary
• client approval and project signature 
• interview with the solvers
• team building

Development and deployment

• building and training models
• performance evaluation
• solution deployment
• output: AI solutions

The self-learning supply chain is an innovative solution in which supply chain systems using artificial intelligence (AI) analyze existing strategies and supply chain data to determine what factors lead to supply chain failure. These AI-based systems then use this knowledge to predict future problems in the supply chain and proactively prescribe or independently implement solutions.

The self-learning supply chain of the future combines the benefits of artificial intelligence with digital technologies that many companies have already started to use. These digital technologies are transforming the very essence of the linear supply chain - volume and scale - into an agile, digitally connected framework that uses artificial intelligence to provide new order fulfillment options.

In a traditional supply chain, many assumptions are made based on human experience and rules, such as estimating production time in a process or machine setup time.

This leads to inaccuracies that lead to unreliable plans. It starts with a small deviation - a technological operation takes longer than planned - and soon, thanks to the domino effect of accumulating inaccuracies, the whole plan is invalid.

In contrast, a self-learning supply chain using artificial intelligence (AI) learns about business processes by continuously analyzing historical data and generates predictions of key performance indicators and recommendations for improving process planning. For example, it captures the difference between a planned and an ongoing task, analyzes the cause of this difference, and uses this knowledge in future planning to reduce this variance.

The core technology will be AI-powered software that fully automates the programming of CNC machines and achieves much faster manufacturing cycle time than is possible using existing software, resulting in correspondingly lower costs.

There are two fundamental problems in the CNC industry today. The first is that you have to program the machine before you start making the components. This is very complex, time consuming and requires great skill. This makes prototypes and small series very expensive because the costs cannot be spread. For low-volume production, NC programming can take days or even weeks and account for 90% of the cost. Also, many qualified CAM programmers are retiring and there is no replacement for them.

The second is that there are trillions of ways to CNC manufacture even simple parts, but only some of them are fast. Humans are good at finding a solution that works, but finding the optimal solution is much, much harder. Our experience shows that most components are produced less than half as fast as they could theoretically be.

The goal of the project will be to automate the programming of CNC machine tools and optimize the resulting machining program so that we can reduce the cost of manufacturing components by half. 

• The system will take advantage of the extensive genetic databases made available by lowering the cost of genetic scanning and an artificial deep neural network that will be trained to predict the structure of proteins from its genetic code. 

   

  A protein's structure dictates its function, and once a protein's shape is understood, its role in the cell can be predicted and in vitro diagnostics developed accordingly. The ability to predict the shape of a protein computationally from its genetic code - rather than determining it through costly experiments - could help speed up the development and research of new in vitro diagnostic preparations.

   

  Main benefits:

   

• Improving understanding of our understanding of the body and how it works
• Designing new, more effective in vitro diagnostic preparations
• More efficient discovery of in vitro diagnostic products and reduction of costs associated with experimentation
• Improving the quality of life for millions of patients worldwide
• Standardization of the development process of new in vitro diagnostic preparations

• Traditional methods of designing new materials are subjective and based on trial and error, and are therefore costly and time-consuming.

   

  The costs of developing a new alloy for special purposes can therefore reach more than 200 million CZK.

   

  The goal of the project will be to develop a system using artificial intelligence for the modeling, development and optimization of new superalloys that will simultaneously meet a whole range of physical requirements.

   

  The system will use an artificial deep neural network that will be trained on pre-existing material data that allows predictions of individual material properties (phase content, fatigue life, yield strength, tensile strength, oxidation resistance, cost, etc.) as a function of material composition and technological production procedure, which will enable the optimization of the material composition so that the multi-criteria target specifications of the resulting superalloy exceed the specifications of existing commercially available alloys.

   

  Main benefits:

   

• Optimizing the composition of new alloys with regard to target criteria such as resistance to corrosion, fatigue stress, weight or cost
• A simulation environment for virtual experiments that will allow narrowing down the search space for new alloys - there are more than 10^40 possible non-patented combinations of alloys
• A digital platform for material data modeling