Research predicts artificial intelligence (AI) will drive an additional $15.7 trillion in global economic growth by 2030.1 Leverage the power of AI with T-Systems’ end-to-end services for building, deploying, and scaling tailored AI and LLM solutions. T-Systems’ AI Foundation Services (AIFS) is the only European platform enabling regulated industries to innovate securely and at scale, ensuring full data privacy and control.
T-Systems’ AIFS drives innovation across industries and functions with practical AI use cases that streamline processes and improve efficiency, while ensuring security and compliance.
T-Systems’ AIFS forms a robust ecosystem designed to accelerate, scale, and safeguard your AI journey. We collaborate with leading AI and technology partners to strike the right balance between operational efficiency and regulatory assurance. With pre-configured models, in-built safety guardrails, and flexible deployment options, we enable seamless integration of AI across business functions while meeting strict compliance and sovereignty requirements. Our ecosystem combines infrastructure, platforms, and applications to deliver secure, scalable, and domain-ready AI for real-world impact.
Our AI solutions are hosted in T Cloud, ensuring compliance with regulations such as GDPR - ideal for regulated industries. Leveraging a resource pool of over 1,500 global data and AI experts and years of experience, we offer more than 400 use cases and integrate over 40 AI models. With a vendor-agnostic setup and open architecture, we help you choose the right AI model, customize components as per business needs, and deploy faster using pre-configured modules and expert integration support. We maintain a state-of-the-art stack, ensuring you have full control over your data.
You can display all external content on the website at this point.
I agree that personal data may be transmitted to third-party platforms. Read more about this in our privacy policy.
The AI Engineer reduces the development process from six months to just a few minutes – including testing and deployment. Simply with a short natural language prompt. As part of our AI Foundation Services, the AI Engineer exemplifies the technological leap we’re witnessing in AI, cutting costs and helping our clients unlock their full business potential.
Creating an AI service involves identifying the right use case and AI strategy, selecting or training a model, deploying it on secure infrastructure, and integrating into workflows to handle specific operations. AIFS simplify this process by providing production-ready components for model deployment, customization, and compliance with regulatory standards. AI Services enable organizations to access, adopt, and scale AI faster and more efficiently, bridging the gap between advanced AI research and practical business applications.
Agentic AI refers to autonomous, goal-driven AI systems that can make decisions and take actions on behalf of humans. They are adaptive, learning from interactions and improving over time. Unlike basic AI, they can collaborate with other systems to manage complex tasks optimally. This brings huge potential in areas like assistants, healthcare, and transport, enabling them to run entire digital ecosystems within these industries for more integrated and effective experiences. They are often powered by advanced LLM models such as GPT-4, Claude, Gemini, and LLaMA.
AI-as-a-Service (AIaaS) is a cloud-based model where businesses can access AI tools and functionalities from third-party providers without having to build or manage their own AI infrastructure, making AI adoption flexible and cost-effective.
LLM serving refers to the process of deploying large language models (LLMs) for use in real-world applications so they can respond to real-time requests efficiently. With AIFS, enterprises can choose multiple open- and closed-source LLMs, unified via a single API, securely across different cloud environments.
Retrieval-Augmented Generation (RAG) combines information retrieval with generative AI for generating relevant outputs. It pulls relevant data from trusted sources like organizational knowledge and databases and feeds it into the model to produce context-aware responses. This improves accuracy, reduces hallucinations, and makes AI outputs more reliable for enterprise use cases.
Fine-tuning adapts an AI model for a specific domain or task by continuing its training on a smaller, specialized dataset. This helps the model understand industry- and business-relevant terms, workflows, and contexts. With AIFS fine-tuning, enterprises can create tailored models that deliver more accurate and enterprise-aligned results.
¹PWC, The potential impact of Artificial Intelligence in the Middle East, 2025, pwc.com and other sources
