Vienesse AI Platform - EXPERT DEEP DIVE (EN)

Why this webinar matters

Many organizations start experimenting with generative AI tools. But quickly, critical questions arise:

Where is our data stored – and who has access to it?
How do we ensure GDPR and EU AI Act compliance?
How can we scale AI usage without exploding costs?

This is exactly where a dedicated AI platform becomes essential.

What you will learn

1. AI Platform - Data sovereignty & compliance by design

Full control over your data, models, and infrastructure
GDPR- and EU AI Act–ready setup from day one
Reduced risks from third-party access and vendor dependencies

2. From tools to real business applications

Move beyond “chat tools” to integrated workflows
Use your own data securely (e.g. via RAG)
Connect AI with your existing systems and processes

3. Flexible and future-proof architecture

Model-agnostic setup (no vendor lock-in)
Scalable infrastructure tailored to your IT landscape
Fast rollout across teams and use cases

4. Sustainable cost model

Pay-per-token instead of expensive seat licenses
Transparent cost logic tied to actual usage
Significant savings potential at scale

What makes the Vienesse AI Platform different

Central platform combining models, agents, workflows, and governance
Built for enterprise use – not just experimentation
Designed to integrate seamlessly into your existing IT environment
Enables real productivity gains instead of isolated pilots

Agenda (approx. 60 minutes)

Introduction & current state of GenAI
Key challenges: data sovereignty, costs, compliance
Deep dive: Vienesse AI Platform architecture
Compliance overview (GDPR, EU AI Act)
Live demo with real business use cases
Cost & ROI comparison
Q&A

Who is the Vienesse AI platform suitable for?

The platform is designed for companies that want to use AI productively without compromising on data protection, governance, or cost control. It is particularly relevant for organizations with sensitive data, multiple business units, and a growing use of AI.

What is the difference compared to ChatGPT Teams or Copilot?

Our platform is built for company-wide use with greater control: custom model selection, clear access rights, auditability, internal knowledge sources, workflows, and agent-based use cases. This gives you more control and less dependency on a single provider.

Do we retain control over our data?

Yes. The platform is designed with data sovereignty in mind. Depending on the setup, data remains within your environment or within clearly defined, controlled hosting and integration structures. Roles, permissions, and data access can be managed precisely.

What does the typical implementation process look like?

We usually start with a brief assessment of your requirements, prioritize meaningful use cases, and define the target setup for governance, models, user groups, and integrations. This is followed by a controlled rollout with clear responsibilities and team enablement.

How quickly can we go live?

This depends on integrations, security requirements, and the desired feature scope. An initial productive deployment is often possible much faster than a full company-wide rollout. We recommend a pragmatic start with prioritized use cases.

Can we integrate our own documents and internal knowledge sources?

Yes. Internal documents, policies, knowledge bases, and selected systems can be integrated so employees can access company-relevant knowledge more reliably. Permissions and data access should be carefully considered.