// ABOUT Myvaltrix
Built on solid ground.
Built for real work.
Myvaltrix is a Kuala Lumpur-based AI consultancy that delivers practical, well-documented AI solutions to Malaysian organisations.
← Back to HomepageHow Myvaltrix came about
Myvaltrix was founded in Kuala Lumpur in 2019 by a small group of data engineers and machine learning practitioners who had spent their early careers inside larger technology firms. The pattern they kept encountering was the same: organisations had meaningful data, clear questions, but no straightforward path from one to the other.
The name reflects an intention. A Myvaltrix is the base on which something more substantial rests. Our job is not to be the visible centrepiece — it's to make sure the foundation is reliable enough that the things your organisation builds on top of it will stand.
We've kept the practice deliberately small. Our engagements are scoped rather than open-ended, our deliverables are documented rather than opaque, and we don't take on more work than we can handle with care. Most of our clients come through referrals, which we take as a reasonable indicator that the approach works.
Mission
To make AI systems that are worth building — ones that earn their place through usefulness, transparency, and honest accounting of what they can and cannot do.
Vision
An AI landscape in Malaysia where organisations can deploy intelligent systems with confidence — because the work behind them was done with care.
Values
Intellectual honesty, appropriate scope, clear documentation, and the willingness to say when something isn't the right fit for a client's situation.
The people at Myvaltrix
Reza Hanafi
Co-Founder & Principal AI Engineer
Ten years of applied machine learning across financial services and enterprise data platforms. Leads knowledge graph and fraud detection engagements.
Yee Lian
Co-Founder & AI Ethics Lead
Academic background in data ethics and policy, with practical experience reviewing AI systems for regulatory compliance. Leads all ethics review engagements.
Amir Rashid
Data Engineer
Specialises in data pipeline architecture and graph database infrastructure. Works across all service lines on the technical implementation side.
How we work — and why it matters
Data Handling Agreements
All client data access is governed by a signed Data Processing Agreement before any work begins. We handle only what's necessary, and destroy copies at engagement close.
Documented Deliverables
Every engagement ends with a handover document explaining design decisions, known limitations, and maintenance guidance. Code without context is of limited value.
Version-Controlled Work
All code and model artefacts are maintained in version-controlled repositories throughout the engagement, with clear commit histories provided at handover.
PDPA Compliance
Our data practices are aligned with Malaysia's Personal Data Protection Act 2010. We advise clients on PDPA implications where their systems touch personal data.
Milestone Check-Ins
No black-box delivery. Progress reviews happen at defined intervals so clients can raise questions, adjust scope, or redirect efforts before the end of an engagement.
Ethical AI Commitment
We refuse engagements where the intended use of AI presents clear risks of harm or unfairness — regardless of the commercial opportunity. This isn't unusual; it's professional standard.
Applied AI in the Malaysian context
Malaysia's digital economy is maturing. Organisations across financial services, e-commerce, research, and government are sitting on more structured and unstructured data than they can meaningfully act on without AI-assisted processing. Myvaltrix exists in that gap — not to replace internal teams, but to carry work that requires focused AI expertise that isn't always sensible to build in-house.
Our knowledge graph work has helped research-heavy organisations connect dispersed datasets in ways that surface non-obvious relationships. Our fraud pattern analysis engagements have given financial and e-commerce clients detection capability that grows more accurate as their operational data accumulates. Our ethics reviews have helped organisations identify where their AI decision-making carries more risk than they'd recognised.
We're based in Kuala Lumpur and understand the regulatory, cultural, and operational context of doing this work in Malaysia. That local knowledge matters when we're advising on data governance or scoping what a model should and shouldn't do.