Building LLM Applications
RAG architectures, agents and MCP, output evaluation, security and on-premise LLM deployment.
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Infrastructure
On-Premise AI Infrastructure
For industries that cannot move data off-site, such as banking, insurance and the public sector, we build AI and LLM infrastructure entirely in your own data centre. Where banking regulations require critical systems to stay in-country, you get the benefits of GenAI without compromising data security.
What we offer
- GPU requirements analysis and hardware sizing
- Open-weight LLM selection, deployment and optimisation
- High-performance model serving (e.g. vLLM) and scaling
- On-premise RAG and document intelligence platform
- Access management, logging and security
When you need it
- Regulation (e.g. banking rules) or security prevents you from using cloud LLM services
- You want to use GenAI on sensitive documents
- You want to keep costs under control at high usage volumes
How we work
Requirements
We define use cases, volumes and security requirements.
Design
We design the hardware, model and software architecture.
Deploy
We set up the infrastructure and optimise models.
Operate
We establish monitoring, update and support processes.
What you get
- LLM infrastructure running on your premises
- Hardware and architecture document
- Performance and cost report
- Operations and security documentation
Related courses
MLOps: Taking Models to Production
CI/CD, model versioning, monitoring and data drift, plus LLMOps for LLM applications: keeping models reliable in production.
Learn more →Responsible AI, Regulation & Data Security
The EU AI Act, Turkish data protection law, Türkiye's AI Action Plan, model risk and privacy: a framework for deploying AI in a compliant and trustworthy way.
Learn more →Frequently Asked Questions
Which models can run on-premise?
Open-weight models with licences suitable for commercial use. The choice is made by testing on your own data for task performance, language ability and hardware needs.
How much hardware is needed?
It depends on model size, usage volume and concurrent users. We size the hardware after a requirements analysis.
Can our existing servers be used?
That depends on GPU capacity. We assess your current hardware and report on its suitability and any additional investment needed.
Do you provide support after deployment?
Yes. Monitoring, update and support processes are defined as part of the deployment.
Related articles
GenAI in Banking: On-Premise or Cloud?
Banks adopting GenAI must balance data localisation, security, model quality and cost. A comparison of on-premise, cloud and hybrid approaches, with a focus on Türkiye.
Read more →Deploying LLMs On-Premise: Hardware, Model Choice and Cost
You can use large language models without your data leaving the building. Model choice, GPU sizing, serving software, security and the cost case for on-premise LLM deployment.
Read more →What Is RAG? Building an AI Assistant on Your Company Documents
RAG lets language models answer based on your organisation's own documents. How it works, how it differs from fine-tuning, what to watch when building it and how to measure quality.
Read more →Let's talk about your project
Tell us about your training, consulting or software needs and we will get back to you within 2 business days.