Enterprise AI architecture
Enterprise AI architecture: five layers and three control planes
A practical enterprise AI architecture framework covering five runtime layers, three control planes, and the questions that expose production gaps.
In-person course
Enterprise AI Architecture Bootcamp
From GenAI PoC to secure, governed and observable production systems.
01 / The outcome
What you will be able to do
By the end of the day, you will be able to design and defend a bounded, secure, governed and observable enterprise AI system—not just an application that calls an LLM API.
Learning outcomes
- Decide when to use GenAI, deterministic software or a hybrid design
- Create a vendor-neutral reference architecture with explicit trust boundaries and control planes
- Design identity-aware RAG with citations, abstention and authorization filtering
- Apply threat modelling and least privilege to models, tools, data and actions
- Define evaluation gates, SLOs, telemetry and cost controls
- Explain architecture trade-offs through Architecture Decision Records
02 / The experience
Practical architecture work, not passive theory
One intensive day built around a realistic Alpine Private Bank case, five team exercises and a production-architecture capstone. The emphasis is on architecture decisions and trade-offs, not framework demos or live coding.
Programme
- Frame the use case, autonomy boundary and non-functional requirements
- Design the platform, trust boundaries and architecture decision records
- Ground the system with identity-aware RAG, citations and abstention
- Control security, identity, governance and privileged actions
- Operate with evaluation, observability, service objectives and cost controls
- Bring the decisions together in a capstone and practical action plan
03 / What you take away
Included course materials
- A participant workbook for the exercises and capstone
- Reusable architecture canvases and an architecture decision record
- A secure-RAG design and threat-model structure
- A production architecture and a focused 30-day action plan
04 / Course fit
Is this course for you?
Check the intended audience and prerequisites before booking.
Designed for
- Solution, software, cloud, security and data architects
- Tech leads, senior engineers and AI platform engineers
Prerequisites
- Ability to read architecture diagrams and reason about APIs, identity and distributed systems
- High-level familiarity with LLMs, embeddings and APIs; no coding, mathematics or vendor certification is required
05 / Related insights
Explore the architecture before the course
Start with a practical article drawn from the same teaching method.
Secure RAG
Secure RAG architecture: authorize before you retrieve
Design secure, identity-aware RAG with authorization before retrieval, evidence lineage, safe citations, abstention, and separate retrieval evaluation.
06 / Date and booking
Upcoming dates
Dates to be announced
Upcoming public dates will appear here once confirmed. Contact Anton to discuss the course, or request private training for your team at your location.