Delivering AI Software in Regulated Swiss Contexts
by Roman Oechslin, Co-Founder & CTO
Regulated delivery is not only a compliance problem. It is an engineering discipline problem.
When requirements include auditability, risk control, and operational continuity, delivery must be structured from day one. The most common failure mode is to treat governance as a late-stage documentation task. In practice, governance and architecture need to move together.
1. Start with delivery traceability, not only features
Before implementation, define decision records, acceptance criteria, and ownership boundaries. This creates a shared contract between business stakeholders, technical teams, and procurement or governance functions.
2. Build architecture around integration reality
Most organizations do not start from a greenfield stack. AI systems need to fit existing data landscapes, identity models, and operational processes. Architecture quality is measured by integration success, not by diagram quality.
3. Design pilots for rollout readiness
Pilots should answer two questions: does the solution create value, and can the organization operate it safely at scale? A pilot that cannot transition to an operating model is only a demonstration.
4. Treat operations as part of delivery
Monitoring, incident handling, handover, and support are core deliverables. They should be specified in scope and reviewed like any other technical output.