What is shardana.ai and why does it have this name?
shardana.ai is an applied artificial intelligence project born in Cabras (Oristano), Sardinia, on April 1, 2026. It brings AI into the real processes of companies and startups. Its name draws cultural inspiration from the Shardana and their association with the island: here it identifies a contemporary technology business, distinct from the historical subject of the Shardana or Sherden. The project brings together web application development, process automation and the integration of AI into business systems. Its story starts with the experience of its founder, Maurizio Brioschi, and the decision to build useful products around concrete problems. The starting point might be a repetitive procedure, an application that needs to evolve or an idea to test with an initial version. Technology is chosen according to these needs, the available data and the people who will use it. This page introduces the studio, its approach and its relationship with DOMOS Network; the founder profile describes the professional background behind the initiative.
What experience supports the work of the studio?
shardana.ai builds on the experience of Maurizio Brioschi: over 25 years in software engineering, backend development, system architecture and technical team leadership. His background includes complex web platforms, business applications and scalable digital products, with work across the software lifecycle. Architecture, development, infrastructure, continuous integration and code review form part of the same responsibility: turning a need into a system that can be maintained over time. This professional experience predates the studio and should not be confused with the age of the brand. His background includes PHP, Laravel, MySQL, API-driven architectures and technologies such as Node.js, React, Vue.js and TypeScript. For a project, the value lies in connecting code decisions with reliability, security and the ability to evolve. That connection keeps the technical result tied to the business problem it needs to solve and to the people responsible for operating and maintaining it after delivery.
How are software and AI projects approached?
The approach starts with understanding the problem: who performs the activity, which information they use, where delays occur and which errors have a real cost. This analysis defines an initial scope that can be tested, with expected results and responsibilities that the people involved can understand. A prototype or MVP helps test assumptions before extending the system. Development proceeds through incremental releases, code review and CI/CD pipelines, with attention to security, maintenance and operating costs. Modular, service-oriented or event-driven architectures make sense when they reduce complexity; they are not independent objectives. For an AI integration, the same approach means identifying where data comes from, which operations are allowed and when a decision must return to a person. Quality is assessed through the behaviour of the system inside the real process, beyond the success of an isolated demonstration or a single response produced under favourable conditions.
Where can applied artificial intelligence be useful?
Applied AI can help people consult company knowledge, organise information and automate repetitive steps between different tools. shardana.ai works on LLM integrations, agentic workflows and process automation, connecting these capabilities to software engineering experience. An assistant becomes useful when it has relevant information, respects permissions and produces answers that people can check. A workflow also needs clear operating boundaries: which data it reads, which actions it can perform and which cases require manual handling. These are design criteria, not promises of complete autonomy or guaranteed savings. A deterministic automation may be the right choice when explicit rules solve the problem more simply. Initial discussions therefore consider the process, data quality, existing integrations and future maintenance together. The intended result is practical support for people's work, with identifiable technical responsibilities and a way to improve performance through verification, feedback and evidence from actual use of the system.
How are shardana.ai and DOMOS Network related?
DOMOS Network is a distinct shardana.ai project focused on building a technology network in Sardinia. It explores community, training, coworking and local partnerships, with a project base in Cabras (Oristano) and the Sinis area. The initiative is being validated: the physical Cabras hub is not open yet. This distinction matters when understanding what is available today and what belongs to the future development of the project. The studio designs and develops software and AI systems; DOMOS explores how developers, technical teams and local partners can connect around work and knowledge. The tech hub page describes the initiative in the context of shardana.ai, while the DOMOS website provides its dedicated space. These destinations are connected but retain their own identities and addresses. To understand the current state of the initiative, readers can consult those references and contact the people building it before making plans or assuming that a physical service is available.
How can you verify the studio's identity and start a conversation?
The brand is shardana.ai, also known as Shardana AI; its legal owner is Maurizio Brioschi, with Italian VAT number 01246820953. It is based in Cabras (Oristano), Sardinia. The details on this page distinguish the business, the person leading it and its related projects. The founder profile includes a portrait, role and professional links, while the studio's official profiles provide additional references for checking the same identity. To start a discussion, describe the current process, the result you want and the tools already in use, without having to specify a technical solution immediately. For an existing application, integration and maintenance constraints also matter; for a new idea, it matters what you want to learn from the first version. Delivery times and costs depend on the agreed scope. An institutional page cannot replace that assessment with an estimate that would be meaningful for every possible project and operating context.