Edison AI Academy · Builders
AI Associate Architect
You can engineer AI systems. Now architect AI products that ship.
A structured program for students ready to design AI-powered systems, workflows, and product prototypes with greater technical and strategic depth.
- Age range
- 15–22
- Duration
- 38 weeks · 4 terms
- Format
- Hybrid
- Level
- Advanced
- Cohort
- 8–12 students
- Outcome
- Shipped AI system + capstone
What is the AI Associate Architect program?
The AI Associate Architect is Edison AI Academy's most advanced engineering program, for students aged 15–22 who have completed the AI Agentic Specialist or equivalent. Across 38 weeks, students learn full-stack AI product development — modern vibe-coding tools, React front-ends, MCP architectures, databases, authentication, payments, and analytics — and ship products that real people use.
Students run the complete arc from idea to a deployed, user-tested, iterated product. Capstone work at this level is indistinguishable from professional work, and graduates leave with a portfolio of shipped products and real users.
Systems, automation, and technical implementation.
A structured program for students ready to design AI-powered systems, workflows, and product prototypes with greater technical and strategic depth.
The AI Associate Architect is Edison's most advanced engineering track. Students stop building projects and start shipping products — software that real people use.
The work is full-stack AI product development: modern vibe-coding tools, React front-ends, MCP architectures, databases, authentication, payments, and real launches. Students run the complete arc from idea to a deployed, user-tested, iterated product.
Capstone work at this level is indistinguishable from professional work. A graduate leaves with shipped products, real users, and a portfolio that needs no allowance made for the age of its author.
- What this program is
Systems, automation, and technical implementation.
- Who it is for
Students who want to move from using AI tools to designing AI systems.
- What students build
Shipped AI system with technical defence
- What students learn
Students architect end-to-end AI systems with mentor sponsorship, design agent workflows, and ship a shipped capstone that operates in the real world.
- How students are taught
Small cohorts of 8–12 students, mentor ratio 1 mentor : 5 students. Edison runs on case-based learning, rotating studio roles, prototype-first modules, and year 3 exhibition.
- Final outcome
Shipped AI system + capstone
The Edison Learning Engine
How a program assembles into capability.
Edison programs are not a sequence of lessons. They are a learning engine, six parts that fit together to produce thinkers who can build with AI, defend their work, and lead.
Assembly
- 01
Curiosity Core
Students begin with questions, not templates. They learn to investigate problems before reaching for tools, the genuine intellectual engine of the program.
- 02
AI Fluency Layer
Students learn how to use AI systems as thinking partners, research assistants, and creative collaborators, across prompts, models, tools, and workflows.
- 03
Builder Studio
Students move from ideas into prototypes, turning abstract concepts into tangible AI-powered outputs through structured, hands-on experimentation.
- 04
Systems Thinking Ring
Students learn to see the whole system: users, constraints, incentives, workflows, ethics, and impact, the connective tissue that turns parts into outcomes.
- 05
Communication Lens
Students learn to present their thinking clearly, explain how their solution works, defend their decisions, and refine in response to critique.
- 06
Final Prototype Engine
Every module ends with a working prototype that students can demonstrate, explain, and improve, the assembled output of the entire learning engine.
The learning pathway, term by term.
Thirty-eight weeks across four terms, more than 150 hours. Every term ends with something shipped to production and used by real people.
- T01
Weeks 1–10
Term 1 — Full-Stack Foundations
The product engineering mindset, React, Tailwind and component libraries, vibe-coding tools, Supabase and PostgreSQL, and APIs and webhooks — applied to a first AI product shipped to production.
- React
- Vibe-coding
- Supabase
- API design
Artefact A shipped AI product MVP
- T02
Weeks 11–20
Term 2 — Advanced AI Architecture
Custom MCP servers, advanced agentic patterns, workflow orchestration, streaming and real-time features, and third-party integrations — built into a subscription AI SaaS product.
- MCP servers
- Agentic patterns
- Real-time AI
- Integrations
Artefact A subscription AI SaaS product
- T03
Weeks 21–30
Term 3 — Product Leadership
Product strategy and roadmapping, AI-specific UX, data-driven decisions, and product ethics — alongside a team product launched in an investor-style pitch to industry judges.
- Product strategy
- AI UX
- Analytics
- Go-to-market
Artefact A launched team product
- T04
Weeks 31–38
Term 4 — Capstone + Exhibition
A signature product built to live in the real world — market-validated, architected, shipped, and presented at graduation to a panel of industry judges.
- Product architecture
- Launch
- Investor-style pitching
Artefact Year 3 signature product
Stop building projects. Start shipping products.
Each build runs the full arc — idea, architecture, front end, back end, authentication, payments, launch. Real users, real analytics, real iteration.
- 01
System diagram
- 02
Shipped capstone
- 03
Technical defence
Shipped, reviewed, and documented — the portfolio of an engineer, not a student.
Students learn with the tools modern builders actually use.
The AI Associate Architect toolkit is curated to match the depth and ambition of this program. Students learn to choose, combine, and switch between tools, not memorise a single platform.
Category
AI Engineering
- ChatGPT
- Claude
- Cursor
Category
Prototyping & Deployment
- Lovable
- Replit
- GitHub
- Vercel
Category
Data & Systems
- Airtable
- n8n
- Make
Tools evolve. Edison teaches the durable thinking, choosing the right tool, combining tools well, and switching when a better tool emerges.
Edison AI Academy
Founding faculty
Students learn from instructors who combine AI fluency, curriculum design, strategy, and practical implementation experience. Edison AI Academy is built around the belief that young people do not just need to learn tools, they need to learn how to think, build, and lead with them.
Cohorts are deliberately small (8–12 students, mentor ratio 1 mentor : 5 students), so every student is known, stretched, and held to a high standard.
We teach young people how to think, build, and lead with AI, not just how to use it.
Edison AI Academy is a selective program. Fees reflect the small cohort size, mentor ratio, and the depth of the work students complete.
Flexible payment plans may be available for accepted students. Bursaries and scholarships are reviewed individually as part of the admissions conversation.
We frame this as an investment in your child's future readiness, not a transactional fee for content delivery.
AI Associate Architect — answers we get asked most.
Not sure if AI Associate Architect is the right fit?
Speak with Edison AI Academy about your child's goals, current skill level, and best-fit pathway. We will recommend the right program and the next available cohort.
Three short steps. We'll match you to the right Edison pathway.
Our admissions team reviews every application personally and replies within one business day. The form takes about two minutes.
Your AI Associate Architect preference travels with you to the application form — we'll know exactly what you're interested in.