An AI architecture course in eight modules on hybrid retrieval, multimodal pipelines and enterprise systems, ending in agents that coordinate without supervision. Instructor-led, on site or live online, for teams already building with LLMs.
This AI architecture course is Level 2, leads to an AI architect certification and builds directly on the Foundations Certificate. Participants should already be comfortable with TypeScript, LLM integration, retrieval basics and simple agent patterns before they start.
The people designing the system around the model — retrieval strategy, service boundaries, memory and how agents talk to each other.
Whoever runs it once it is live: authentication for AI endpoints, observability, monitoring and the injection surface nobody thought about.
Developers who have already shipped features with LLMs and now have to design the system around them.
A prototype survives a demo. Production breaks on hybrid retrieval, knowledge graphs and agents that have to coordinate, the parts a basic RAG course never reaches. Each module of this AI architecture course is a standalone engineering discipline.
Chain-of-Thought, ReAct, Self-Ask and Tree-of-Thought implemented in TypeScript — thought‑action‑observation cycles, agent scratchpads and prompt injection defence.
Learning objectiveEngineer the reasoning, not just the promptEvery intermediate step can be inspected and audited, rather than trusted as a black box.
Certify Your TeamThe advanced RAG course inside the program: vector retrieval combined with knowledge graph reasoning — GraphRAG on Milvus, entity linking, mutual indexing and relationship traversal.
Learning objectiveRetrieve on meaning and on structureAnswers cite the facts and the relationships between them, not just the closest match.
Certify Your TeamApplications that handle text, images, audio and video through ModelFusion — any-to-any architectures, Weaviate, Gemini and on-device deployment.
Learning objectiveBuild across every modalityOne TypeScript abstraction for every input type, instead of a separate pipeline for each.
Certify Your TeamType-safe AI functions with TypeAI, generics for reusable components, retry with exponential backoff and caching strategies for model responses.
Learning objectiveMake model calls reliable and reusableComponents the whole team can share, that recover from failures and cut repeat cost.
Certify Your TeamScalable microservices, authentication and authorisation for AI endpoints, observability hooks, monitoring dashboards and serverless deployment.
Learning objectiveStand systems up to enterprise requirementsDelivered on AWS, GCP or Azure, and ready for the review a demo never faces.
Certify Your TeamPrompt-centric interfaces in React and TypeScript — template forms, history management, streaming handlers, Zod-validated prompts and A/B testing.
Learning objectiveDefend the attack surfacePrompt injection defence and input sanitisation from the interface inward, because production AI is a high‑value target.
Certify Your TeamReAct agents with multi-tool state, LangGraph streaming, tiered memory across short-term buffers and long-term vector stores, and multi-agent protocols.
Learning objectiveCoordinate agents that run unsupervisedDomain‑specialised agents that hand work to each other across long workflows, without a person in the loop.
Certify Your TeamAn advanced build and live presentation — a multimodal RAG and KAG system, an autonomous agent suite, or an enterprise application with full observability.
Learning objectiveDefend the architecture you builtDesigned, deployed and argued for live, the way it would be in a real design review.
Certify Your TeamVector search alone is not enough, and it is where most RAG course material stops. Knowledge graphs, entity linking and structured reasoning are treated as first-class retrieval, not as an add-on.
Chain‑of‑Thought, ReAct and Self‑Ask are implemented from scratch in TypeScript, so the team understands exactly why each pattern works.
Authentication, observability, serverless delivery and monitoring. Production systems have requirements that demos never surface.
Every pattern is considered through the lens of autonomous agents. Tool use, memory and coordination run throughout rather than sitting in one module.
Systems that reason across images, audio and video through unified TypeScript abstractions and any-to-any model architectures.
Prompt injection defence, sanitisation middleware, auth for AI endpoints and secure data handling, from the first module.
More than twenty quizzes and scenario assessments through the AI architecture course, plus the hands-on build that closes each module.
An advanced application built and presented live, deployed with the observability and security the rest of the program taught.
An AI architect certification: a Gen AI Architecture Certificate per participant who completes the AI architecture course and passes the capstone.
The design decisions behind systems that survive production: an advanced RAG course on hybrid retrieval and knowledge graphs, multimodal pipelines, and agents that coordinate without a person supervising each step.
15+ hours across eight modules, instructor-led, on site or live online, with 20+ quizzes and a final capstone for the AI architect certification.
The Gen AI Architecture Certificate. Each participant who completes the AI architecture course and passes the capstone earns it as a verified credential that anyone can check.
Engineers and technical leads who have already built with LLMs and now have to make the architecture hold under real load and real data.
It is the natural first half. Teams that already ship LLM features can start the AI architecture course directly; teams that do not usually take both as the Gen AI Stack.
An architecture they have designed and defended, plus the Gen AI Architecture Certificate for those who pass the assessments.
The AI architecture course is built for the group and priced on team size, level and timeline.
Build production-grade agentic AI in TypeScript — LLM integration, RAG pipelines, agents and a capstone you deploy and keep.
Learn More →Both engineering tracks as one path — from TypeScript fundamentals through to autonomous systems, with two capstones.
Learn More →21 hours building production multi-agent systems — anatomy, orchestration, guardrails and a launch review.
Learn More →The prototype already works. This is where your team makes it survive real data, real load and a security review.