Teaching AI
with Intention
Loomlogic was built around a single conviction: that learning AI development works best when every topic connects to the next.
Back to HomeFrom Scattered Resources to a Structured Path
Loomlogic grew out of a frustration that many of our founders had experienced personally: the AI learning landscape was full of standalone tutorials, isolated workshops, and courses that each assumed different prerequisites. You could spend months working through material and still feel like the pieces didn't hold together.
In 2022, a small group of developers and educators based in Bangkok started mapping out what a coherent curriculum would look like — one where each course assumed and reinforced the one before it, and where the final output was something a learner could actually show someone. After a year of design, revision, and piloting with small cohorts, Loomlogic launched its first public intake in mid-2023.
The name reflects the approach. A loom structures threads so that each one interlaces with the others to create something that holds together. That's what we try to do with the curriculum: connect concepts deliberately so the whole is more useful than any individual part.
Mission
To offer a connected, project-focused AI development curriculum that takes learners from their first line of code to a portfolio they can stand behind.
Approach
Every course is designed to build on the previous one. Concepts are introduced with working examples, and learners leave each module with something they built, not just notes they took.
Where We Operate
Our administrative base is in Chatuchak, Bangkok. All courses are delivered online, and we work with learners across Southeast Asia and beyond.
People Behind the Curriculum
Somchai Krairat
Co-founder · Curriculum Director
Former data engineer with ten years in enterprise analytics. Designed the Practicum's data preparation modules and oversees overall course sequencing.
Naphat Wongchai
Co-founder · Lead Instructor
ML practitioner and educator. Leads the Starter Course and Capstone mentorship sessions, with a background in applied NLP and model deployment.
Prae Thongchua
Operations & Learner Experience
Manages enrolments, scheduling, and learner communication. Previously worked in EdTech operations at a Bangkok-based platform serving regional markets.
How We Hold Ourselves Accountable
The standards below aren't aspirations — they're checkpoints built into how we design and deliver each course.
Curriculum Review Cycle
Course content is reviewed every six months against current tooling and frameworks. Outdated material is updated before the next intake opens.
Data Privacy Compliance
Learner data is handled in accordance with Thailand's Personal Data Protection Act. We do not share personal information with third parties for marketing.
Feedback-Driven Iteration
Every learner completes a structured review at course end. Aggregate feedback informs the next revision cycle — not just as data, but as editorial input.
Practitioner-Written Content
All course material is written or reviewed by people who have worked on actual AI projects, not adapted from generic programming tutorials.
Accessible Instructional Design
Materials follow plain-language principles: technical terms are explained on first use, examples precede abstractions, and no prior academic background is assumed.
Honest Scope Descriptions
Course descriptions state exactly what is and isn't covered. We'd rather set the right expectations up front than have learners feel misled after enrolling.
What Shapes the Way We Teach
AI development is a field where the gap between reading about something and being able to do it is unusually wide. A learner can work through a comprehensive textbook on neural networks and still struggle to prepare a clean dataset for a real project. That gap — between conceptual knowledge and working practice — is where most self-directed learning falls short.
Loomlogic's curriculum is built to close that gap deliberately. The Starter Course doesn't introduce data science as an abstract discipline; it introduces it through a series of small, working tasks that a beginner can complete in an afternoon. By the end, learners have built something, not just described how building would work. The Practicum extends that into longer guided tasks where the challenges are more open-ended. The Capstone Track asks learners to define and execute a project of their own — with a mentor who can redirect when the approach isn't working.
We operate out of Bangkok, which shapes some of our editorial choices. Southeast Asia has a strong and growing community of developers working on AI applications across logistics, agriculture, finance, and civic services. Our examples and case studies draw from that context where relevant, without limiting the curriculum to regional concerns.
The English-language delivery is intentional. Most AI tooling, documentation, research, and community discussion happens in English. Learning the subject in the language in which it's practised is a practical advantage, not an obstacle.
Want to Find Out More?
Reach out and we'll walk you through which course suits your current level and what to expect from the experience.
Contact Us