A school built around finishing things
We started Neuralforge because most AI education stops at the point where the real work begins. We wanted a place that stays with learners all the way to a working, documented outcome.
Back to HomeOur story
Neuralforge was founded in 2021 by a small group of engineers and technical writers who had spent years running internal AI workshops at Bangkok-based product companies. The common thread in those workshops was always the same: people understood the concepts well enough but could not complete a project without a structured external scaffold.
The school was built to provide that scaffold. We run three programs — a twelve-week project course, a three-month cohort program, and an annual reading library — each designed to take someone from an idea to a documented, working outcome. We are not a certification factory and we do not sell the idea that finishing a video course makes you an ML engineer.
Our sessions are held in Chatuchak, Bangkok. Remote participation is supported across all programs. The cohort is capped at sixteen people by design — mentorship rounds only work at that scale.
Mission
To help engineers and analysts build one applied AI tool they understand end-to-end and can explain to another person.
Approach
Project-first. Every program is oriented around producing a documented output — not a portfolio of screenshots and a certificate PDF.
Values
Small cohorts. Honest scope. Good writing. We think the discipline of documenting what you built is as important as building it.
The team
Practitioners who have shipped real systems and want to help others do the same.
Pichit Khamwong
Co-founder & Program Director
Ten years building recommendation and classification systems at Thai fintech firms. Designed the project course curriculum from scratch.
Siriporn Rattana
Cohort Lead & Mentor Coordinator
Former data science lead at a Bangkok logistics company. Runs group sessions and coordinates the mentorship rounds each cohort cycle.
Ananya Charoenwong
Editorial Lead, Reading Library
Technical writer and editor with a background in NLP research. Curates the library reading list and moderates monthly discussion sessions.
How we maintain quality
Standards we hold ourselves to across every program we run.
Structured curriculum review
Course content is reviewed after every program cycle. We update examples, reading lists, and project prompts based on what participants found useful or confusing.
Mentor qualification
All mentors have verifiable industry experience in applied AI, data engineering, or ML systems. We do not bring in academic researchers who have not shipped production software.
Project documentation standard
Every final project report follows a consistent structure. We provide a documentation template and review drafts before participants submit their final version.
Data and privacy practices
Participant data is stored only for program administration. We do not share enrolment or project information with third parties or use it for marketing.
Honest feedback loops
Participants submit anonymous program feedback after each cycle. We publish summary findings internally and use them to decide what to change next time.
Scope honesty
We tell applicants clearly what each program does and does not cover. If a program is not a good fit for someone's background, we say so before they enrol.
Applied AI education in Bangkok
Neuralforge operates from Chatuchak, Bangkok, and runs programs that target engineers and analysts who already have some technical background and want to work with applied machine learning in a structured context. The school does not run introductory programming courses, and it does not award paper credentials for watching recordings.
The three programs cover different levels of engagement. The project-based Python course is the most structured: twelve weeks, weekly checkpoints, and a documented deliverable at the end. The cohort program adds group mentorship, a shared reading schedule, and small-group sessions with practitioners. The reading library is the lightest-touch option — an annual pass with editorial curation and one discussion session per month.
Working professionals make up most of the student body. Lawyers, product managers, data analysts, and backend engineers have all completed the project course. The cohort is especially useful for people who find self-directed learning hard to sustain — the external pacing and peer accountability make a real difference.
The school keeps cohort sizes small by design. Sixteen people is the ceiling for the mentorship program. That cap exists because mentorship rounds — where a mentor works with a small group on a specific part of their project — stop functioning meaningfully above that number.
Want to know if a program fits your situation?
We respond to all enquiries within one business day. Tell us your background and what you want to build.
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