Among our core competencies belongs utilization and custom research development of Artificial Intelligence software and machine learning technology in the enterprise applications. Actually, Creatity began as an Artificial intelligence startup. Optimize and automate processes, save costs, time and human resources or personalize customer service.
The main idea is simple – why to write a script where you need to specify every single condition and each reaction to it? You can let the AI learn based on available data and determine what the best reaction is. This is more accurate, faster and at the end of the day more cost-efficient approach than changing the code each time the situation changes. The human errors are eliminated, decisions are faster and quicker and consistent user experience is provided. The major benefit is that AI can take an action also in conditions that were never present in the training set.
Creatity s.r.o. will help you to identify most suitable use-cases for implementation of artificial intelligence into your business application. Not all use cases are suitable – it depends on available data sets, on the environment and actions that can be taken. We assess also security implications for applications equipped with AI decision engine. It is necessary to have quite a big amount of data for training of AI – even if your company does not possess the necessary data, we can get them for you in certain cases – our developers have know-how in the fields of web-scraping and web-crawling.
Based on historical data we can implement and train neural networks so that they can predict next steps based on past situations. This is possible only if there is a causal dependence from the past to the future and the data set contains enough data for training, testing and validation.
The main approach is very similar – based on available data, deep neural network is able to learn what category to choose for given input data. The architecture of the neural network is here a bit different. CNN in combination with GRU, LSTM or other types of neural networks is usually used. This depends on actual use case.
In this case the data must be somehow marked or tagged and pre-evaluated to show clearly what the desired pattern is, and sufficiently large training set must be present. The neural network is then trained to identify this pattern even in very fuzzy data. However, there must always be correlation between the pattern and input data. Use cases are voice or face recognition, detection of objects in images or video, authentication and identification, etc.
This is like the pattern recognition problem, however, here is the situation reverted. AI is trained to detect a normal situation and classify it accordingly. In case that anything else beyond normal is detected, for instance some suspicious network activity happens, alert mechanism is triggered, and situation may be analyzed.
Data extrapolation and interpolation
Have you even seen those funny mobile applications that can predict how would you look like older or younger? If the answer is yes you have a better idea what we can do with production data. We can fill out missing gaps or interpolate any time sequence to the past or to the future.
In general data harmonization will help to achieve better prediction and optimal estimation of material and costs.
Automating repetitive tasks in your company leads to time benefits and better quality due to standardization. Processes are faster and more effective; the human errors are eliminated. This approach provides opportunity for employees to do more creative and complex work. Your customers will be pleased by having personalized offers of products or virtual assistants.