The need for updating artificial intelligence systems, especially machine learning models, is highly dependent on the variability of the data used (data drift, concept drift), the dynamics of the business environment, the complexity of the model and its purpose. There is no single rule that applies to all, but continuous monitoring is essential.
Factors and considerations determining the frequency of updates:
- Continuous monitoring: continuous monitoring of the model performance (e.g. accuracy, evolution of business metrics), as well as the distribution of input data (data drift) and the change in the relationship between data and the target variable (concept drift) is key. If significant deterioration or change is detected, this may warrant an update (re-training, fine-tuning).
- Changing business environment: the emergence of new products, services, competitors or changes in customer habits may also require models to be reviewed and updated.
- Data-driven updating: in some cases, a review and, if necessary, an update may be carried out at predefined intervals (e.g. quarterly, semi-annually), but this should always be supported by monitoring results, not at arbitrary intervals.
If you want fast deployment, scalability and cost-effectiveness, the cloud is the best choice. However, if the highest level of control over data security, data sovereignty (precise knowledge and control of the physical location of data) or specific IT compliance requirements are paramount, on-premise deployment may be more appropriate. It's important to note that large cloud providers also offer high-level security and compliance solutions, but on-premise gives you complete control over your data assets. Of course, there is also a hybrid solution, which allows sensitive data to be managed on-premises while resource-intensive AI tasks are performed in the cloud.
Cloud-based AI solutions are fast to deploy, flexible to scale and cost-effective, especially when it comes to computationally intensive tasks (e.g. learning large-scale models, complex predictive analytics, running large-scale language models). Local AI systems provide greater control and data security, ideal for sensitive business data. The hybrid model combines the advantages of both approaches.
The scalability of AI-based systems depends on a number of factors, including the model architecture chosen and the technologies used. Using containerization (e.g. Docker), microservices and cloud infrastructure (e.g. Kubernetes, serverless architectures), systems can typically be easily scaled up to multiple business units or dynamically growing data volumes.
AI integration takes place in four main stages:
- Assessment and design: developing business goals, data sources and architecture.
- Implementation: data preparation and processing (building the data pipeline), model development, training and testing.
- Release: gradual roll-out of the AI system.
- Operation: continuous monitoring, model updating and optimisation.
This enables AI agents and other intelligent systems to operate reliably in the business environment.
The first step in implementing AI is to assess the quality and availability of your data sources and the integration possibilities with existing systems. After this, it's advisable to launch a pilot project focusing on a smaller, well-defined business problem. This could be a simpler automation task (e.g., data processing, a customer service chatbot), a predictive analytics model (e.g., predicting customer churn), or a decision-support system for a specific business area.
The application of artificial intelligence is useful in many industries. In finance, for example, predictive analytics and AI-powered decision-making assist business processes. In healthcare, AI-based diagnostics and automated analysis support doctors. In manufacturing, artificial intelligence helps with maintenance, quality control, and report generation. In logistics and marketing, AI agents and AI-powered chat systems increase efficiency and improve the quality of business proposals.
The duration of custom software development depends on the project's complexity, features, and the detail of the specification. A basic mobile application, a CMS-based website built from templates, or a development replacing Excel-based record-keeping systems can be completed in a few weeks. In contrast, a complex system with AI or integration features might take several months or even a year.
Using agile software development methodologies makes the process faster and more flexible. This allows for continuous feedback and fine-tuning, even when following unique business logic.
The cost of development depends on the project's complexity, features, and development time. Basic system development typically starts from 2–5 million HUF, while a complex enterprise solution – such as a reporting or ERP system – can be tens of millions of HUF.
The cost is influenced by:
- The chosen software development methodology and technology.
- Required integrations, such as custom report generation.
- Development time and subsequent operation.
- The possibility of using a modular structure or "template-based" systems (CMS or record-keeping).
We can provide an exact price after a detailed needs assessment and specification.
EN-CO Software Zrt. uses state-of-the-art frameworks and solutions such as ASP.NET, Node.js, WordPress, Drupal, Angular, React, Vue.js and React Native for custom software development. The choice of technology is always tailored to the business needs and the chosen software development models. If required, applications are also supported based on customized report examples.
