Our Story
Learning AI Takes Time.
We Help You Use It Well.
Codexis was built around one straightforward idea: people learn technical skills by practising them, not by watching someone else do it.
How We Started
From Chiang Mai, Built for Online Learners
Codexis opened its doors in Nimmanhaemin — Chiang Mai's creative district — in 2021. The founders had come from software engineering and data backgrounds, and they kept seeing the same pattern: learners who had watched dozens of hours of video content but still could not build anything on their own.
The answer they kept coming back to was structure and feedback. A clearly mapped sequence of work. A mentor who reads your actual code. Exercises that use messy, real data. That is what Codexis is designed around.
Thailand's technology sector has grown steadily, and there is genuine demand for people who can work with AI tools and machine learning methods. Codexis is not trying to fast-track anyone into a new career overnight — the courses are honest about effort required. But for learners willing to put in consistent work, the programme gives them a structured path and support.
Mission
Give people who want to work with AI a structured, honest path to doing that — with real practice, real feedback, and realistic expectations.
Vision
An online school where every learner can point to work they have built — not just a completion badge — at the end of their course.
Values
Honest communication, practice over theory, and genuine support — not automated grading or one-size-fits-all modules.
The People
Who Teaches and Supports You
The Codexis team is small by design. Fewer learners per mentor means more specific feedback and a better experience for everyone.
Aroon Prachit
Lead Instructor, AI & ML
Former data engineer with eight years in production ML systems. Leads the machine learning and advanced AI tracks, and reviews project code personally.
Siriporn Kraikeaw
Programming Track Mentor
Software developer turned educator. Runs the beginner programming track and focuses on helping learners build solid foundations before moving to models.
Natcha Wongkham
Curriculum & Learner Support
Manages course structure, learner onboarding, and day-to-day support. First point of contact for questions about enrolment, content, or platform access.
How We Work
Teaching Standards and Practices
These are the principles that shape how courses are designed and how learners are supported throughout their time at Codexis.
Human Code Review
Submitted exercises are reviewed by a mentor, not an automated checker. Comments address your specific logic and approach, not just whether tests pass.
Staged Module Design
Each track follows a deliberate sequence. Later modules build on earlier ones — learners do not jump ahead until foundational ideas are solid.
Data Privacy
Learner data is handled under a clear privacy policy. Course submissions are used only for feedback and are not shared externally.
Practice-First Design
Course materials prioritise exercises over lecture content. The ratio of reading to doing is deliberately weighted toward hands-on work.
Transparent Communication
Course descriptions are honest about time requirements and difficulty. No course at Codexis promises outcomes we cannot stand behind.
Regular Content Updates
AI tools and libraries change quickly. Track content is reviewed and updated on a regular cycle to keep examples and techniques current.
About Our Approach
Online AI Education That Puts Practice First
Codexis operates as an online school with a Chiang Mai base — which means the team is accessible and responsive, while learners can join from anywhere. The core belief is that technical knowledge in AI and machine learning becomes real through repeated, structured practice on meaningful data, not through passive consumption of theory.
The programming track covers the building blocks that connect general coding to the kinds of data manipulation and numerical thinking that machine learning work requires. The machine learning course moves learners through the full cycle of a real project — from raw data to a working model — with mentor oversight at each stage. The advanced systems track addresses the harder questions of system design, model refinement, and the practical constraints that appear when working at scale.
All three tracks are designed to leave learners with something tangible: a portfolio of code and projects that reflects actual technical capability. In a field where the landscape shifts quickly, we think the most durable thing a course can produce is a learner who can keep building long after the course ends.
Want to Know More Before Enrolling?
Send us a message. We are happy to talk through which track fits your background and what to expect from the course.
Get in Touch