Programmes
Applied AI programmes, shaped to your work
Business and engineering programmes run on Claude or GPT. Every programme is customised to your industry, roles and workflows.
For Engineering and Data Teams
5 programmesProduction-grade AI engineering, from coding agents to architecture.
Advanced AI Capability Programme: Claude for Engineers
Software and AI engineers
40 hours over 10 days
What participants leave with
Production-grade software built with coding agents, from project setup and design to security, evaluation and shipping.
Applied GPT Programme for Engineers
Software and AI engineers
40 hours over 10 days
What participants leave with
Applied development on the latest OpenAI models, tools and frameworks.
FDE Master Track
Forward deployed engineers who deploy AI inside client environments
40 hours over 10 days, with a capstone defended before an expert panel
What participants leave with
The ability to take an AI solution from client problem to production deployment.
AI Architect Pathway
Senior engineers moving into architecture
Prerequisite check, then Foundation, Core and Advanced stages, and a capstone
What participants leave with
The ability to design, govern and scale enterprise AI systems end to end.
AI-Native Application Development
Developers, engineers, architects and tech leads
3–5 day intensive technical workshop
What participants leave with
Production AI systems with the Claude API, OpenAI Responses API and multi-agent orchestration, with security architecture.
Specialisations
Specialist engineering tracks
Each is hands-on in a governed lab with no local setup.
Agentic Systems and MCP
Multi-agent systems that plan, use tools and act, connected to enterprise systems through Model Context Protocol.
A working agent wired to real tools.
RAG and Context Engineering
Retrieval, chunking, re-ranking and context design that holds up at scale.
A grounded assistant on the client's documents.
LLMOps and AI Evaluation
Evals, observability, guardrails, and control of cost and latency.
An evaluation harness for a live use case.
AI Security and Red Teaming
Prompt injection, data leakage, agent permissions and the OWASP Top 10 for LLMs.
A red-team report on the team's own app.
Data Foundations for AI
Pipelines, quality and governance, in three levels: foundations, practitioners, architects.
An AI-ready data blueprint.