Advanced · 2 days
Architecting Enterprise Generative AI
A two-day course for architects and senior technical leaders: design enterprise-scale AI capability as a platform, not as a sequence of isolated projects.
2 days · Advanced · Architecture · Architects and senior technical leaders
Overview
Organisations that succeed with generative AI rarely do it as a pile of unrelated copilots. They build a small number of shared capabilities — identity, model access, retrieval, evaluation, cost control — and let product teams consume them. This course is about that enterprise shape.
You work through enterprise AI platforms, shared services, model gateways, identity, security, governance and data access. RAG platforms, observability, evaluation and cost management are designed as multi-team services. Vendor abstraction, deployment patterns and the split between platform and product responsibilities are made explicit.
The teaching uses architecture case studies. You design a platform that several product teams could use without each team negotiating a model vendor, a vector database and a logging story from scratch.
The result is an enterprise architecture and an operating model, not a project plan for a single assistant.
Audience
Architects and senior technical leaders responsible for enterprise AI capability, platform strategy or multi-team enablement.
Prerequisites
Experience with enterprise architecture, platform engineering or large-scale delivery. Familiarity with generative AI is expected. This is not an introductory course.
Duration
2 days
Often delivered as a private session around your organisation’s current estate and constraints.
Learning outcomes
- 01
Design an enterprise AI platform with shared services for models, retrieval, identity and evaluation.
- 02
Define platform versus product responsibilities so teams are neither blocked nor unsupervised.
- 03
Specify identity, security, governance and data-access patterns that multiple products can share.
- 04
Plan vendor abstraction and model-gateway policy without pretending lock-in is free to avoid.
- 05
Design RAG and evaluation as platforms rather than as features inside one application.
- 06
Establish cost management, observability and enablement for several teams.
- 07
Produce a target architecture and a sequenced path from current isolated projects.
Outline
- 01
From projects to a platform
- Why isolated copilots recreate the same security, cost and quality problems.
- Shared services: gateway, policy, retrieval, evaluation, observability.
- Platform versus product: APIs, paved paths and the work that must stay in teams.
- Case study: an organisation with five assistants and no shared control plane.
- 02
Shared control: identity, security, data and vendors
- Identity, entitlements and acting on behalf of a user across assistants.
- Data access, tenancy and a RAG platform with permissions.
- Governance: model and prompt change, approval, and audit.
- Vendor abstraction, dual-sourcing and the cost of a gateway.
- Exercise: define the shared services and the product-team contract.
- 03
Enablement and operations
- Observability, evaluation and cost allocation across teams.
- Deployment patterns: central platform, federated delivery, and hybrid.
- Multi-team enablement: documentation, golden paths and review.
- Exercise: sequence a 12-month platform roadmap from current projects.
- 04
Target architecture
- A complete enterprise architecture: control plane, data, runtime and delivery.
- Risks: central bottleneck, shadow AI, and a platform that is only a procurement wrapper.
- Case study review and critique.
- Produce a target diagram, responsibility matrix and next-step programme.
Practical work
Architecture case studies and design work. You critique an estate of isolated AI projects, define shared services, and produce a target architecture, responsibility split and sequenced roadmap. Where possible, the case is your organisation’s.
Takeaways
- A target enterprise AI architecture
- A platform versus product responsibility matrix
- Gateway, RAG, evaluation and cost-control service definitions
- A sequenced enablement roadmap
Delivery
Cognitecta delivers private corporate training, on-site or as remote live training. Courses can be run as published, or adapted to your organisation’s stack, domain and experience level.
Instructor
Nicholas Johnson, AI architect and software engineer. He has a degree in Artificial Intelligence and around twenty years of professional technology training, including hundreds of courses for engineering teams and large organisations. About.
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