Services

AI Systems Development

As a part of digital transformation organisations are looking to utilise AI in their systems and operations. To utilise AI, organisations need to understand what AI is and how it fits into their business model. This is not a simple problem. AI development process is a set of steps that includes data preparation, modeling, simulation and test, and deployment.

The key success factor  in AI implementation is identifying issues and understanding in which stages of the building process to spend time and resources. Few important considerations have to be taken before starting the project of building AI system:

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Normally AI is part of a larger system, and it needs to efficiently operate within the system.

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Understand and identify critical components of the environment and dependencies for AI system.

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Some parts of the system may already exist, but there are no integration points.

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At this early stage usually there is a lack of knowledge and experience with AI systems within the organisation.

Data Preparation

AI Modeling

Testing

Deployment

Data
Preparation

AI Modeling

Testing

Deployment

Explore our development journey and see the stages we will undertake in creating a personalized AI solution for you.

Our four-stage development process.

Data Preparation

Data preparation is the most important step in the AI development. Without clean and consistent  data used to train AI model the whole endevour is likely to fail (garbage in garbage out). This can be one of the most time-consuming steps of the process as well. Very often we focus on fine tuning the model instead of reviewing and improving the quality of data.

AI Modeling

Successful AI modeling is going to deliver an AI model that can produce intelligent outcomes based on the data. In this stage we heavily rely on existing services like deep learning and/or machine learning. We also utilise other algorithms such as classification, prediction, and regression during the modelling stage as well as prebuilt models. This is iterative process of training the system where we tune the model and compare outcomes, step by step.

Testing

AI models operate as part of larger system and must provide accurate and reliable results. As for any technical system, testing is essential to prove that the AI model produces correct outcomes in tandem with other parts of the system. In this stage we observe the overall accuracy of performance for given scenarios as well as system’s behaviour for extreme conditions.

Deployment

Once the AI system is ready for depolyment we decide in what kind of environment to deploy and what capacity we require for the production run of the system. Designated hardware and software environments can vary from local servers to the Cloud and/or IaaS. As SaaS specialists we build scalable and highly flexible solutions that enable organisations to deploy their model across a variety of environments without rewriting the code.

Explore our development journey in creating a personalized AI solution for you.