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
Mapping to common services:
In our AI Models Practice, we combine our services to provide a tailored response to meet your specific needs. In this example, we would consider the following services:
Tailored Service Example: Model choice = risk classification, data controls, executive decision‑making
- AI Governance & Ethics
- AI Security & Cyber
- AI Infrastructure & Data
- Executive AI Coaching & Training
Get clarity on your AI model strategy…
Independent, vendor-neutral advice on model selection, from foundation models to fine-tuned and open-source alternatives, matched to your use case, budget and risk appetite.
Free downloads:
- UK Five AI Principles – Key Takeaways
- EU AI Act – Key Takeaways
- Sample AI & Agentic Automation Corporate Usage Policy
- AI Use & Data Boundaries Checklist
Next Steps
- AI Readiness Assessment – Understand where you’re at
- Discovery & Planning – Preparing your business for change
- Design – Example components of a full AI Architecture stack
- Implementation & Adoption – Managing Vision to Execution
Our Practices
Agentic Platforms Supported:
