Agentic AI Pilots Integrated into Enterprise Data Landscapes

Three pharma AI pilots — validated by 30+ users and all selected for rollout — taken from architecture to a production-ready path inside regulated, multi-stakeholder environments.

Challenge

In regulated, multi-stakeholder environments the brief recurs: prove value fast with an AI MVP, but in a way that survives scrutiny and can move toward production. The agentic systems had to be genuinely useful inside the organization — connected to real knowledge and workflows rather than running as isolated demos — and the work required tender-facing architecture and clear scope boundaries so pilot outcomes could be judged by technical and non-technical stakeholders alike.

What we did

We led the design and delivery of agentic AI MVPs, PoCs, and pilots across multiple industries including the public sector, using a full-stack approach (Python plus modern web frameworks) and stakeholder-aligned delivery practices. We architected and implemented multi-agent systems (using ADK) with retrieval-augmented generation (RAG) on Gemini Enterprise and integrated them into client data landscapes.

In parallel we designed Google Cloud solution architectures for RfPs — translating procurement requirements into viable cloud architecture — and, on the applied GenAI side, delivered a marketing-automation application that cut content-creation time from days to under an hour using Imagen.

Result

Three pharma PoCs were positively evaluated by more than 30 users and selected for pilot rollout — a repeatable path from architecture to validated pilot in high-accountability contexts. The RfP architecture work contributed to CHF 100k in deal value, and the fundamentals that separate a demo from a credible rollout — RBAC-style identity and permissions, multi-environment deployment, and security requirements for go-live — were delivered directly with the client.

For a Swiss bank, insurer, or public-sector buyer, that is the point: an agentic use case carried from architecture to MVP, integrated into enterprise data, validated by real users, and engineered to survive go-live.