Enterprise AI infrastructure — cloud-ready, sovereign by design.
Adopt-AI designs and operates AI infrastructure for organizations that require security, compliance, and control. We support AWS and Google Cloud deployments while enabling Swiss-sovereign architectures for sensitive workloads and regulated environments.
Modern AI systems require more than models—they require robust infrastructure. At Adopt-AI, we build production-grade AI platforms that align cloud strategy, data residency, and security requirements. Our architectures allow organizations to deploy AI at scale while retaining ownership of data, models, and operational controls, ensuring long-term autonomy and regulatory alignment.
Flexible deployment aligned with your risk profile.
Every organization has different constraints. We design AI infrastructure that fits your operational, regulatory, and geographic requirements—without locking you into a single provider or limiting future evolution.
Public cloud ready
Deploy AI workloads on AWS or Google Cloud using enterprise-grade security, identity management, and scalable container-based infrastructure.
Swiss-sovereign hosting
Support for Swiss-based cloud environments to ensure sensitive data and processing remain within Switzerland.
Hybrid architectures
Combine sovereign environments with controlled external services when explicitly required, under strict policy enforcement.
Data sovereignty is enforced by architecture.
Data sovereignty is not a statement—it is a technical design choice. We implement AI systems where sensitive data remains within your defined perimeter. For generative AI and advanced analytics, we favor powerful open-source models deployed on controlled infrastructure, ensuring immediate access to compute resources without dependency on non-Swiss external providers.
- Swiss data residency for storage, processing, and backups
- Private model serving for open-source LLMs and deep learning models
- Controlled access policies and traceable data flows
- Clear separation between internal data and optional external enrichment
Data Sovereignty
Your data stays where you define it—protected by architecture, not just policy.
Security and compliance built in from day one.
AI systems must meet the same standards as core enterprise platforms. We design infrastructure with security-by-design principles, ensuring consistency with organizational security frameworks and regulatory expectations.
Single Sign-On (SSO)
Integration using enterprise identity providers
MFA & RBAC
Multi-Factor Authentication and role-based access control
Network segmentation
Private connectivity options and network isolation
Encryption
Data encrypted in transit and at rest
Secrets management
Key rotation practices and secure credential handling
Audit trails
Centralized logging and access traceability
Vulnerability management
Penetration testing coordination and security assessments
Platform Engineering
Modern DevOps practices applied to AI systems for reliability and maintainability.
Production-grade engineering for AI systems.
We apply modern platform engineering practices to AI delivery so systems remain reliable, observable, and maintainable over time. Our approach reduces operational risk while enabling faster iteration and controlled scaling.
- CI/CD pipelines for AI services and applications
- Containerized deployments using Docker
- Kubernetes-based orchestration where appropriate
- Infrastructure-as-Code for reproducibility and control
- Environment separation (development, staging, production)
- Monitoring, logging, and alerting for operational visibility
From experimentation to industrialized AI.
We help organizations move from isolated experiments to fully operational AI capabilities. Our custom pipelines support machine learning, deep learning, and generative AI across the full lifecycle.
- Data ingestion, validation, and versioning pipelines
- Model training, evaluation, and regression testing
- Model registry and controlled release management
- Performance, drift, and cost monitoring
- Human-in-the-loop workflows where required
- Documentation and operational handover to internal teams
MLOps & LLMOps
End-to-end AI lifecycle management for sustainable, scalable AI operations.
Governance & Enablement
Building internal capability and reducing vendor dependency over time.
Built for long-term autonomy.
Beyond infrastructure, we help establish the governance and operating models required to scale AI responsibly. This includes documentation, architectural standards, and team enablement to reduce vendor dependency and support sustainable growth.
- Clear ownership and responsibility models
- AI governance and risk management frameworks
- Knowledge transfer and operational documentation
- Alignment between IT, security, legal, and business teams
Institutional support and ecosystem alignment.
Adopt-AI benefits from initial coaching support through Innosuisse, reflecting our commitment to building robust, innovation-driven, and compliant AI solutions aligned with the Swiss innovation ecosystem.