Adversarial testing of your LLM applications: jailbreaks, prompt injection, exfiltration and misuse scenarios in production.
Security review of AI pipelines: data, models, training, inference and monitoring. Includes threat model and remediation.
Architecture and implementation guidance for secure AI/LLM integrations in your applications: RAG, agents and orchestration.
Detection of model poisoning, adversarial inputs and training data manipulation. Specific to production LLMs and custom models.
Generative AI only works if the integration is well thought out within your workflows. A chatbot that responds to random documents delivers little value; a retrieval-augmented assistant that sits on your policies, procedures and case files and integrates into your existing workflow delivers immediate time savings. We always design AI integrations from a concrete use case: which process is more efficient, which decision is better informed, which knowledge is better unlocked? That question determines the architecture, not the other way round.
A typical project starts with a discovery phase of two to four weeks where we work with stakeholders to define the use case, assess data availability and quality, and build a proof of concept. Next comes a pilot with a defined group of users, where we gather feedback, measure metrics (time savings, satisfaction, error reduction) and fine-tune the system. Only when the business case is proven do we move to production — with monitoring, evaluation, hallucination detection, access control and logging that meets your security and governance requirements.
We work model-agnostically: depending on your requirements around privacy, latency, cost and quality, we choose between cloud-hosted models (Claude, GPT, Gemini) or on-premise alternatives. For organisations that prioritise data sovereignty, we deploy solutions on the European cloud or even fully within your own infrastructure. Security and governance are not an afterthought — we embed them from the architecture phase, so your AI integration immediately meets your ISO 27001, NIS2 and AI Act obligations.
Related Services
Governance as the foundation for safe AI.
LLM security assessment for your AI systems.
Combine with web application penetration testing.