Deploy a Sovereign AI Chatbot on Scaleway : A Technical Deep Dive with Galene.AI

Deploy a Sovereign AI Chatbot on Scaleway : A Technical Deep Dive with Galene.AI Deploy • Angelo Lippolis • 22/07/25 • 6 min read

Before we started building, one thing was clear: our clients must have full control over their data, models, and compliance. This was non-negotiable. So we engineered a fully sovereign AI platform from the ground up, one that empowers companies to adopt our technology with confidence. Thanks to Scaleway’s powerful GPUs and robust infrastructure, we made it happen, delivering true data sovereignty and compliance. —Andrea Cappelletti , Founder & CEO @ Galene.AI Generative AI is transforming how businesses operate, but for many European organizations, adopting powerful large language models (LLMs) comes with a critical trade-off. Using public SaaS platforms means sending your data to third-party ecosystems, often outside the EU. This creates significant compliance risks with GDPR as well as the upcoming AI Act, not to mention issues of data sovereignty, intellectual property, and vendor lock-in.

What if you could run a high-performance, state-of-the-art conversational AI platform, deployed entirely within your own private, sovereign cloud?

This is exactly what Galene.AI Platform, running on Scaleway's GPU infrastructure , delivers. An end-to-end, self-hosted conversational AI stack that gives you full control over your models, your data, and your compliance posture. Let’s take a dive into the technical architecture and the stakes involved.

The Sovereignty Gap in Traditional SaaS AI

While convenient, standard SaaS AI solutions introduce issues that are unacceptable for businesses in regulated industries like finance, healthcare, and manufacturing, or for any public sector entity.

The core problems are :

Data Exposure and Residency : your prompts, documents, and sensitive customer data are processed and potentially stored on servers outside European jurisdiction. The local jurisdiction where your servers are located may, in certain legal decisions, conflict with GDPR or AI Act compliance.

Loss of IP control: The data you (your employees or your clients) use to interact with the model can be used to train future versions of the provider's public model. Your intellectual property can effectively become their training data.

Opaque Operations : you have no visibility into the model’s supply chain, its security posture, or how it handles your data. This "black box" approach makes it impossible to conduct proper risk assessments, guarantee compliance, control accuracy or stability over time.

Vendor Lock-in and Unpredictable Costs : per-seat licenses and consumption-based pricing models can become prohibitively expensive as you scale, creating long-term dependencies with little flexibility. Moreover, they usually forbid you to perform performance optimizations tailored to your use cases.

These challenges are not just theoretical; they are practical barriers to AI adoption for production use cases at scale.

Galene.AI a Sovereign-by-Design Architecture

Galene.AI is engineered from the ground up to eliminate these risks. The Galene platform lets you deploy and run on your own infrastructure, on-premise or in your private Scaleway cloud. This "sovereign-by-design" approach ensures your data never leaves your control.

At its heart, the platform runs as a private Kubernetes cluster , a design that provides scalability, security, and operational flexibility. Here’s how the architecture breaks down.

The Foundation: Private Kubernetes on Scaleway GPUs

The entire Galene.AI platform is containerized and orchestrated with Kubernetes. This ensures a seamless deployment on Scaleway Cloud.

Local Model Execution All AI tasks are processed locally on high-performance Scaleway GPUs (from 2xL40S to H100 based configurations) housed within your virtual machine instances. This eliminates external API calls to third-party models, drastically reducing latency and completely removing data exposure risks.

Encrypted data All data is encrypted in transit (TLS 1.3) and at rest (AES-256), ensuring that your sensitive information is protected at every layer of the stack.

Scalability and Resilience Kubernetes provides the scalability to handle fluctuating workloads and the resilience to ensure high availability, all within your private network.

The Core: Private Agents and the Generative Shield

This is where the platform’s intelligence and governance come together.

1 ) Private AI Agents

Galene.AI relies on a powerful agentic framework. More than chatbots retrieving information, a framework orchestrates configurable agents to perform complex tasks. Thanks to Model Context Protocol (MCP), these agents can

securely access internal knowledge bases (like technical documentation or HR policies),

connect to enterprise systems (CRMs, ERPs), and

execute multi-step workflows with reasoning capabilities.

2 ) Generative Shield - Real-time AI Governance

Built directly into the platform is Generative Shield, an integrated AI governance layer that acts as a firewall for all human-AI interactions. It enforces compliance in real time by monitoring and filtering requests and responses based on four key pillars:

Cybersecurity and Exploitability : protects against prompt injection, adversarial attacks, and data exfiltration attempts.

Ethical and Legal Compliance : ensures adherence to EU regulations and ethical guidelines, preventing the generation of harmful or biased content.

Data Protection and Privacy : enforces strict data handling rules, preventing sensitive data (PII, IP) from being processed or leaked.

Accuracy and Integrity : validates AI-generated outputs against trusted sources to mitigate hallucinations and ensure factual correctness.

By deploying models directly on your infrastructure and wrapping them with Generative Shield, you retain full ownership and control while operating safely within regulatory boundaries.

The Interface: User-Facing App and Developer API

Galene.AI is built for both business users and developers, offering a flexible, three-layer access model

The User Interface: a clean, ChatGPT-like conversational UI allows non-technical users to interact with the platform using natural language, upload documents for analysis, or create personal assistant agents.

The Agentic Layer: the is where you configure and manage private AI agents (defining their skills, knowledge sources, and operational rules via the Model Context Protocol (MCP)) while...