Flagship AI architecture course

Designing Production-Ready Enterprise AI Applications

A practical architecture course for teams that need to move from GenAI prototypes to reliable applications with governance, quality metrics, privacy controls, budget discipline and operational ownership.

Most enterprise GenAI effort stalls at the prototype-to-production gap. The demo works, the architecture doesn't: no pattern for where RAG ends and agents begin, no decision on memory and state, no guardrails between the model and sensitive data, no answer to "what does this cost per user and per month", and no operating model for who owns it when it breaks. This course treats production readiness as a design discipline — the architectural decisions, guardrails and operating practices that separate a reliable enterprise application from a fragile demo.

Over three days you learn the enterprise application patterns that recur across real deployments — chat, RAG, agents, classification, summarization and workflow automation — and when each is the right call. You design a RAG architecture end to end (ingestion, chunking, metadata, embeddings, hybrid search, citations and freshness), make deliberate choices about memory and state with privacy boundaries intact, and layer in guardrails and policy: input and output controls, access control, content safety, data-leakage prevention and review gates. Cost and performance get the same rigour as features — token budgets, caching, batching, model routing, latency and capacity planning — and the course closes with monitoring and operations: traces, quality metrics, incident playbooks and a release process you can actually run.

You leave with a production architecture you can apply immediately: the pattern selection framework, the guardrail and policy checklist, the cost model and the operations plan — plus the decision records that show your thinking. It is designed for architects, senior developers and platform teams who are past the demo stage and need a defensible design for their next enterprise AI build, and for technical leads who will own the application once it ships. By the final day you will be able to design a GenAI application end to end — from pattern choice and data architecture through guardrails, budget and operations — and justify each decision to the reviewers who approve it.