School of AI & Technology · Mentogram

The people building
the future of AI
learned it here.

Not a video course. Not a bootcamp. The School of AI is built from exclusive knowledge from the engineers, product managers and AI leaders who are shipping real AI systems today — taught by Maya, 24/7.

Apply Now →See All Programs
5
Programs
12
Specialised competencies
24
Concepts per competency
$8K
Starting price
24/7
Maya always on
The Opportunity Right Now
AI skills command a 4.1x salary premium. Most people have none.

LinkedIn Learning 2025 data: AI-skilled professionals earn 4.1x more than peers without AI skills. 8.5 million AI roles will go unfilled globally by 2030. The gap between what companies need and what people know has never been wider.

Most AI courses teach you to use tools. They don't teach you to think in systems, build agents, or transform organisations. That gap is exactly what the School of AI fills.

Generic AI Course
$200–$500
Tools-focused, no depth, no credential
AI Bootcamp
$5K–$15K
Cohort-based, no personalisation, rushed
Self-taught (YouTube)
Free
No structure, no evaluation, no credential
School of AI
From $1.5K
Practitioner knowledge · Maya 24/7 · Real credential
Your AI Mentor

Our AI is structured to
make you think like an AI Pro.

Maya is trained on exclusive interviews with AI engineers, ML leads and AI product managers shipping real AI in production. Not tutorials. Real systems thinking.

Stage 1 · Hook
M
⏱ 5-MIN CHALLENGE · A02
Your AI support agent is hallucinating order details in production. It's live. You have 5 minutes. What are your first 3 moves?
Kill the agent, route to humans. Pull last 100 outputs. Check if it's retrieval or generation failure.
M
Safety before diagnosis. Most people debug first. You contained it first. Moving to Stage 2.
Stage 5 · Quiz
Concept A03 · RAG Systems
Your RAG system returns semantically similar but factually wrong chunks. The most likely cause is:
AEmbedding model is too small
BChunking strategy breaks context across boundaries✓
CVector database has too many dimensions
DTemperature is set too high
Why B: Large chunks that span topic boundaries create embeddings that blend meaning — similar in vector space but wrong in context.
Stage 7 · Evaluation
Maya's Evaluation · A02 Agent Architecture
Concept Score
81/100PASS
System Design19/20
Tool Selection16/20
Error Handling14/20
Scalability17/20
Cost Awareness15/20
Strong: Orchestrator design correctly separates routing from execution.
Fix: No fallback when sub-agent fails. Add graceful degradation before production.
Stage 4 · Scenario · A11
AI Transformation Decision
The CFO wants AI to reduce headcount by 30% in 18 months. The CTO says the data infrastructure isn't ready. You're the AI Lead. What do you do?
ASide with the CFO — the business case is clearhigh
BSide with the CTO — pause until infrastructure is readymedium
CPropose a phased plan: fix data first, automate secondlow
DBring in a third-party audit before decidingmedium
Stage 7 · Eval · A02
Concept Mastery · AI Agents & Agentic Systems
Maya has reviewed your agent architecture submission. Here's your performance breakdown.
87
Strong Pass
You demonstrated solid understanding of agent memory systems and tool use. One gap identified below.
Agent Architecture92%
Tool Use & Memory88%
Multi-Agent Orchestration74%
Planning Loops91%
Maya's Note
Your multi-agent orchestration score suggests you're conflating CrewAI and AutoGen patterns. Next concept will address this directly.
Why This Is Different
AI is moving too fast for static courses. Maya moves with it.

Maya's knowledge base is updated as practitioners are interviewed. When the AI landscape shifts — and it shifts every month — the curriculum reflects it.

🤖
Agentic learning — not passive watching
Maya challenges, probes and evaluates. Every session is an active dialogue across 8 structured stages.
🔒
Mastery-gated. Score ≥72 or you do not advance.
No shortcuts. Every concept is evaluated across system design, reasoning, cost awareness and communication.
⚡
Always current
Trained on exclusive practitioner interviews. Updated as AI evolves. Not a recorded course from 2023.
🎯
Calibrated to your background
Maya knows if you are a developer, a PM or a business leader. She teaches accordingly.
🧪
8 learning interfaces
TIMER, SCENARIO, QUIZ, TASK, EVAL, DASHBOARD, DRAGDROP, SIMULATION — Maya picks the right one for every concept.
The Competency Library

12 AI competencies.
Each one built from practitioners.

Each competency = 24 concepts = ~180 minutes of structured Maya-led learning. Every concept includes a real deliverable you must pass to advance.

Specialised AI Competencies (A01–A12)
A01
AI & LLM Fundamentals
How LLMs work, transformer architecture basics, context windows, tokens, model capabilities and limitations, AI landscape 2025
A02
AI Agents & Agentic Systems
Agent architecture, tool use, memory systems, planning loops, multi-agent orchestration, LangChain, CrewAI, AutoGen
A03
Prompt Engineering & LLM APIs
System prompts, chain-of-thought, few-shot learning, RAG systems, Claude/OpenAI/Gemini API, evaluation frameworks
A04
Machine Learning for Business
Classification, regression, clustering, model evaluation, feature engineering, ML project lifecycle, AutoML tools
A05
Data Strategy & Engineering
Data architecture, ETL/ELT pipelines, data governance, dbt basics, modern data stack (Snowflake, BigQuery), data quality
A06
Business Intelligence & Dashboarding
Tableau/Looker/Metabase, KPI design, dashboard architecture, self-serve analytics, storytelling with data, BI strategy
A07
AI Product Management
Building AI-native products, AI-first UX, model selection, latency/cost trade-offs, hallucination management, AI roadmapping
A08
Automation & No-Code Systems
Make/Zapier/n8n workflow design, no-code tools (Webflow, Airtable, Notion), AI-powered automation, measuring ROI of automation
A09
AI Ethics, Safety & Governance
Bias in AI, explainability, AI regulation (EU AI Act, US EO), responsible deployment, model auditing, AI governance frameworks
A10
NLP & Language AI Applications
NLP use cases, text classification, sentiment analysis, summarisation, information extraction, search, RAG system design
A11
AI for Business Transformation
AI strategy, identifying AI opportunities, build vs buy decisions, change management for AI adoption, measuring AI ROI
A12
Computer Vision & Multimodal AI
Image recognition, object detection, video AI, multimodal LLMs (GPT-4V, Claude), use cases across manufacturing, retail, healthcare
Generic Competencies — Shared Across All Schools
G01Strategic Thinking
G02Leadership & Management
G04Data Analysis & Interpretation
G05AI Tools for Professionals
G06Problem Solving & Frameworks
What You Actually Build

Real AI systems.
Not toy projects.

Every concept ends with a real task. You design agents, build RAG pipelines, write AI strategies — Maya evaluates you the way a senior AI engineer would.

Maya · Task60 min
Stage 6 — Task · A02
Design a Multi-Agent Customer Support System
You are the AI lead at a Series B fintech. Support tickets have grown 10x in 6 months. Design an agentic system to handle 80% of tickets autonomously.
Deliverables
Agent ArchitectureTool DefinitionsHandoff LogicEvaluation FrameworkCost Estimate
Maya · Task60 min
Stage 6 — Task · A03
Build the RAG Pipeline for a Legal Knowledge Base
A law firm wants to query 10,000 case documents using natural language. Design the full RAG architecture — chunking, embedding, retrieval and generation.
Deliverables
Chunking StrategyEmbedding Model ChoiceRetrieval LogicPrompt DesignEvaluation Metrics
Maya · Task60 min
Stage 6 — Task · A11
Write the AI Transformation Roadmap
The CEO of a 500-person logistics company asks you to identify 5 AI opportunities, prioritise by ROI and build a 12-month implementation plan.
Deliverables
Opportunity AuditROI FrameworkBuild vs Buy Matrix12-Month PlanSuccess Metrics
Programs

From builder to strategist.
Pick your path.

Five programs across the full AI stack — from building agents to leading AI transformation.

MBAMost Comprehensive
AI MBA
12 competencies · $8K–$10K · 12–18 months

The complete AI professional. From LLM fundamentals to agent architecture to AI strategy. Graduate with the skills to lead AI in any organisation.

Apply for AI MBA →
A01AI & LLM Fundamentals
A03Prompt Engineering & LLM APIs
A02AI Agents & Agentic Systems
A07AI Product Management
A11AI for Business Transformation
A04Machine Learning for Business
A05Data Strategy & Engineering
A09AI Ethics, Safety & Governance
G06Problem Solving & Frameworks
G01Strategic Thinking
G04Data Analysis & Interpretation
G05AI Tools for Professionals
PGP6 competencies · $4K–$5K · 6 months
PGP in AI Agents

Purpose-built for builders. Design, build and deploy autonomous AI agents from simple tool-use to complex multi-agent orchestration.

A01A02A03A10A09G05
PGP6 competencies · $4K–$5K · 6 months
PGP in Data & Analytics

For analysts who want to go deeper. Data strategy, ML fundamentals, BI, NLP — the full analytical stack.

A04A05A06G04A10G06
PGP6 competencies · $4K–$5K · 6 months
PGP in AI Strategy for Leaders

For non-technical leaders who need to make AI decisions. AI literacy, transformation playbooks, governance and ROI measurement.

A11A01A09G01G02G06
CERT3 competencies · $1.5K–$2K · 2 months
Certificate in Automation & No-Code

Build powerful AI-powered workflows without writing code. Make, Zapier, n8n, and prompt engineering — in 8 weeks.

A08A03G05
The Practitioners Behind Maya

Designed by
Mentogram Mentors

Engineers, PMs and AI leaders who have deployed production AI. Their exclusive knowledge lives only here.

Andrew Chow
School Director
Andrew Chow
Managing Partner · Asia Pro Ventures
A Message from the School Director
“Most AI education teaches you to use tools. What we built at Mentogram teaches you to think in systems. The practitioners in this school have shipped real AI in production — not demo projects, not Kaggle competitions, but systems that serve real users at scale. If you want to lead AI in any organisation, this is where you build that foundation.”
Tools You Will Master

The actual stack.
Not theory about it.

Every concept in the School of AI is taught using the tools practitioners actually use. By the time you finish, these are part of your working vocabulary.

LLM
Agents
Vector DB
Automation
Data
BI
API
Models
No-Code
Dev Tools
🤖
Claude
LLM
🤖
GPT-4
LLM
🤖
Gemini
LLM
⛓
LangChain
Agents
🕸
LangGraph
Agents
👥
CrewAI
Agents
🌲
Pinecone
Vector DB
🔍
Weaviate
Vector DB
⚙️
Make
Automation
🔄
n8n
Automation
⚡
Zapier
Automation
❄️
Snowflake
Data
📊
BigQuery
Data
🔧
dbt
Data
📈
Tableau
BI
👁
Looker
BI
🔌
OpenAI API
API
🔌
Anthropic API
API
🤗
Hugging Face
Models
🔁
Replicate
Models
📋
Airtable
No-Code
🌐
Webflow
No-Code
🛠
Retool
No-Code
✏️
Cursor
Dev Tools
Tools are covered within their relevant competency. You learn them in the context of real problems — not as standalone tutorials.
The window is open. For now.

AI skills compound.
Start earlier than everyone else.

We review every application personally. If accepted, you receive your Student ID within 48 hours and begin your first AI concept immediately.

Apply to School of AI & Tech →
Reviewed within 48 hours
Student ID issued on approval
Begin learning immediately