dvloper.io, a company specialising in infrastructure, software development, data, security and automation, has invested over €500,000 in AI, research and development, and in creating a technological framework for companies wishing to move from pilot projects and experiments with artificial intelligence to the implementation of AI solutions in their business processes. The model, known as AI Factory, aims to standardise recurring components and processes in artificial intelligence projects and enable their reuse in new projects.
The company estimates that, this year, projects involving the development and implementation of AI solutions for clients will generate revenue of approximately 1.7 million euros. At the same time, artificial intelligence is already integrated into dvloper.io’s operational model, and 95 per cent of the company’s development and implementation processes are carried out using AI.
AI Factory is a technological and operational framework comprising architectures, reusable components, processes and know-how, and was launched on the basis of experience gained from projects in fields such as software development, infrastructure, data, security, automation and AI. With the launch of AI Factory, the company is targeting, in particular, organisations that have identified opportunities to use AI but do not yet have a clear architecture and process for transforming these opportunities into functional solutions in production.
“Many companies want to adopt AI, but the question remains: where do we start, and how do we move from an experiment to a system that works in production? AI Factory seeks to standardise precisely this journey. Instead of rebuilding every project from scratch, we reuse components, architectures and processes validated in previous projects – from data access and models right through to security, observability and the mechanisms through which humans retain control over important decisions,” said Mihai Chihaia, Chief Revenue Officer at dvloper.io.
AI Factory is the result of projects carried out by dvloper.io in fields such as technology, infrastructure and the public sector. The company states that, in certain projects, AI agents have been used to bring together knowledge from multiple fields and technologies, whilst human specialists have remained responsible for validating the results and making the final decision. In some of the projects developed by the company, AI agents were used to combine expertise from multiple technologies and fields, including in contexts where the level of specialisation and the number of skills required would be difficult to find in a single person. However, the role of AI is not to replace the specialist, but to extend their analytical capabilities, whilst the specialist retains responsibility for validation and the final decision.
AI Factory’s objective is not to build as many AI agents as possible, but to create a repeatable, secure and controlled method by which they can be integrated into organisations and produce measurable results.
“In recent years, a great many companies have been experimenting with AI, building proof-of-concepts and trying out different technologies. Now the question is starting to shift from ‘what can we do with AI?’ to ‘how do we make AI work in practice within our organisation?’ We’ve noticed that many of the problems that arise when firms start integrating AI into their operations are recurring, regardless of the industry. We have accumulated components, architectural models and know-how that it no longer made sense to rebuild from scratch for every project. AI Factory emerged at the intersection of these two factors: companies want to move from experimentation to the practical use of AI, and we have reached a level of expertise where we can standardise a significant part of the journey towards that goal,” added Mihai Chihaia.
Over the next 12 months, dvloper.io aims to increase the number of projects built on AI Factory and to transform the experience gained into increasingly mature and easily reusable components and models. The aim is to bridge the gap between experimenting with AI and integrating it into real business processes, in a secure, controlled manner that delivers measurable impact.
