At Fluentia, AI-powered software development is a structural operating model that cuts across the entire software lifecycle, with the entire development process designed with AI in mind, step by step. The model is based on repeatable, standardized steps in which AI is an integral part of the entire development process. People make decisions – AI provides analysis, options, and speeds up the process. Development work is faster, more deliberate, and more predictable from idea to production.
For customers, this means above all a more manageable development project: faster progress from idea to production, better decision-making in the early stages, and fewer errors and surprises in the later stages. At the same time, the cost and risk profile remains more predictable.
Development always begins with understanding. Idea formulation, market needs assessment, user understanding, and goal clarification are carried out in a structured manner with the support of artificial intelligence.
Artificial intelligence prepares, for example:
At each stage, people have access to collected, refined, and analyzed information to support their decision-making. AI also ensures that decisions made are documented so that they can be utilized later in the project. From the customer's perspective, this means that decisions are not made based on assumptions alone. A realistic picture of costs, schedules, and technical options is created at an early stage. People make go/no go decisions, but these decisions are based on a broader and more analyzed knowledge base, with AI-generated documentation and AI-native system and technical specifications serving as the basis for implementation.
Implementation does not start from scratch, as AI solutions effectively break down large definitions into logical entities that are easy to implement with AI enhancement. Implementation can be started quickly, and the solution can be easily developed iteratively until it truly meets the customer's needs.
Artificial intelligence improves efficiency in implementation in the following ways, among others:
We use agent solutions that, for example, generate code and tests, perform quality assurance, submit code for approval testing, and, if necessary, even for production. However, responsibility for architecture, information security, and the overall system always lies with people.
For the customer, this translates into faster progress without compromising quality. Repeatable practices and additional analysis generated by artificial intelligence, on the other hand, reduce the likelihood of errors.
Quality assurance is integrated into the entire development process. CI/CD practices, automated testing, and static and agent-based analysis form a continuous quality checkpoint.
Artificial intelligence is used, for example, in:
Manual testing is only used in a targeted manner to supplement automated testing.
Moving into production does not mean that software development needs to end; rather, the software must evolve alongside the business. Maintenance, monitoring, log analysis, exception handling, and continuous development are key parts of our model.
Artificial intelligence improves efficiency in the maintenance phase, for example:
When the development model is built from the ground up to be AI-native and standardized, maintenance is also manageable. The customer gains transparency into what is happening, why it is happening, and what to do next.
An AI-powered, systematic development model enables you to do the right things at the right time – in a controlled manner. It brings structure to ideation, speed to implementation, and predictability to maintenance.
People still control the process, but AI is changing the way software is designed, built, and maintained. In a structural model, AI is not a separate addition, but an integral part of the entire life cycle – from specification to continuous development.
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