AI Models (SLM/LLM)
Choosing and controlling AI models with intent
Practical support for model selection, risk classification, prompt patterns and RAG design — underpinned by proportionate governance.
What we do
The rapid growth of LLMs has made model choice and usage complex and risky. We help organisations make informed decisions about which models to use, how to use them, and how to control them — aligned to data sensitivity, risk appetite and regulatory expectations.
Our work focuses on practical deployment patterns rather than experimentation for its own sake.
Practice features
- Model selection and comparative assessment
- Risk classification and usage constraints
- Prompt patterns and safe interaction design
- RAG architecture considerations
- Governance, monitoring and assurance controls
Outcomes
- Reduced risk from inappropriate model use
- Clear rationale for model and architecture choices
- Models aligned to real business use cases
CTA: Get clarity on your AI model strategy
Mapping to common services
Common services applied:
- AI Governance & Ethics
- AI Security & Cyber
- AI Infrastructure & Data
- Executive AI Coaching & Training
Example: Model choice = risk classification, data controls, executive decision‑making