The AI Forge · Mastery · F8
Enterprise AI Architecture
Multi-model strategies, data architecture, multi-tenant, edge, scaling 1→100 agents, platform
- Duration
- 2 days · 9:00–17:00 each day (13h contact, incl. breaks + lunch)
- Participant level
- Advanced: experienced practitioners
- Format
- Instructor-led design labs
- Participants
- 8 included · 12 maximum (flexible group size)
- Prerequisite
- F6 + F7 (or equivalent)
- Ecosystem
- Agnostic: any model provider or stack
- Price
- EUR 8,500 (EUR 450 per extra participant)
Amounts in EUR, excl. VAT.
ai-generatedWhat participants will be able to do
- Design a scalable, sovereign enterprise AI architecture
- Choose between cloud, hybrid, and on-premise LLM deployment
- Apply zero-trust security patterns to AI infrastructure
- Build an architecture decision record for AI systems
Tools & resources
What changes after this module
Architects design enterprise AI platforms that scale, stay vendor-flexible and self-serve, multi-model routing, sound data architecture, multi-tenancy and fleet-scale agents, using architectural patterns that persist across model and vendor change.
Who should attend
Engineers and data professionals. Advanced: experienced practitioners.
Programme
This agenda is indicative. Content, sequencing, and examples are adapted to your team's context, tools, and objectives.
Before you start
- A laptop with a working development environment and admin rights
- Proficiency in at least one general-purpose language (Python and/or JavaScript/TypeScript)
- A code editor/IDE with an approved AI coding assistant enabled
- An API key for an approved model provider (issued by the company)
- A Git repository and command-line comfort
- Completion of F6 + F7 or equivalent
- A real or representative enterprise scenario to architect against
| Day 1 | 9:00–10:30 | AI-era architecture patterns: Microservices, event-driven and serverless for AI workloads; where each fits; avoiding lock-in by design Lab: Choose and justify an architecture pattern for a real AI workload: the enterprise scenario you’ll architect across both days: and diagram it. |
| 10:30–10:45 · ☕ Coffee break | ||
| Day 1 | 10:45–12:30 | Multi-model strategy: Routing, fallback chains, cost/quality trade-offs and vendor diversification: durable insurance against any one model Lab: Design and stand up a model router with fallback and measure cost/quality across models. |
| 12:30–13:30 · 🍽 Lunch break | ||
| Day 1 | 13:30–15:30 | Data architecture for AI: Lakehouse, streaming, feature and vector stores at scale, and data governance/sovereignty Lab: Design the data + retrieval architecture for the scenario, with governance and sovereignty constraints: decide what data may leave the jurisdiction and what must stay, before the architecture hardens around it. |
| 15:30–15:45 · ☕ Coffee break | ||
| Day 1 | 15:45–17:00 | Lab: reference architecture: Assemble the target architecture Lab: Produce a reviewed multi-model reference architecture with a component diagram: the living artefact every later lab extends. |
| Day 2 | 9:00–10:30 | Multi-tenant & edge: Tenant isolation, cost allocation, per-tenant compliance; on-device/edge inference and when to use it Lab: Extend the architecture for multi-tenancy and add an edge/on-device path where it fits. |
| 10:30–10:45 · ☕ Coffee break | ||
| Day 2 | 10:45–12:30 | Scaling 1→100 agents: Orchestration, resource management and observability across a fleet of agents Lab: Design the scaling + observability approach for a fleet and stress-test the plan against failure. |
| 12:30–13:30 · 🍽 Lunch break | ||
| Day 2 | 13:30–15:30 | Internal AI platform: Self-service, golden paths, shared guardrails and evals as a platform capability Lab: Define the internal AI platform: self-service surface, golden path and shared guardrails. |
| 15:30–15:45 · ☕ Coffee break | ||
| Day 2 | 15:45–17:00 | Roadmap & wrap: Build-vs-buy, migration and a staged platform roadmap; peer review Lab: Present the architecture + a staged platform roadmap for challenge. |
Deliverables
- Multi-model reference architecture + diagram
- Model router with fallback (cost/quality measured)
- Data + retrieval architecture with governance
- Multi-tenant + edge extensions
- Fleet scaling + observability design
- Internal AI platform definition + staged roadmap
Interested in running this module for your team? Get in touch and we'll tailor the format, dates, and delivery to your context.
Request this trainingYour trainer
Senior expert
Has designed multi-model enterprise AI platforms and scaled them in production
Discovery Session
This module requires a full day to deliver a meaningful learning experience. We only offer it in its complete format.
Frequent questions
Can modules be taken individually?
Yes. Every module stands alone at a fixed price, and every module counts toward a programme if you continue.
Where does training happen?
At your premises or remote, on your dates, for private cohorts. Open sessions run on a fixed monthly calendar.
Which AI tools do you train on?
Yours. Every module ships in four ecosystem editions and runs its exercises on your real stack.
Who delivers?
Inforca's senior consultants and trainers. Flagged modules and the executive track are delivered at senior-expert level.
Discuss this module in a First Call: fit, dates, and the path around it.
No commitment · our team responds within one business day