Our Story
How novusynars Came to Be
novusynars was founded in Kuala Lumpur by a small group of data engineers who had spent years watching the same pattern unfold at company after company: ambitious AI projects derailed by fragile, poorly structured data infrastructure. The models were often fine — it was the plumbing underneath that kept failing.
We started novusynars with a straightforward premise — that well-designed data infrastructure is the single most important investment an organisation can make before deploying machine learning at scale. Not the most glamorous work, perhaps, but the kind that determines whether AI initiatives actually succeed in production.
Since then, we have worked with organisations across sectors in Malaysia, helping them move from ad-hoc data practices to structured, maintainable systems. Our focus has always been narrow on purpose: we do data infrastructure for AI, and we aim to do it well.
Our Mission
To help organisations build data systems that are resilient, well-documented, and genuinely fit for AI workloads — so that the models and applications running on top of them can be trusted.
Our Approach
We collaborate closely with your in-house teams rather than operating as a black box. Every architecture decision is documented, explained, and designed with long-term maintainability in mind.
Our Team
The People Behind the Pipelines
A compact team of data specialists, each with deep experience in building and maintaining infrastructure for AI systems.
Amir Kadir
Lead Data Architect
Over a decade of experience designing data platforms for financial services and e-commerce companies across Southeast Asia.
Sarah Lim
MLOps Engineer
Specialises in feature stores and model serving infrastructure. Previously worked on recommendation systems at scale.
Raj Nair
Senior Pipeline Engineer
Focused on building robust ETL/ELT workflows with strong monitoring and observability. Background in telecommunications data.
Standards
Quality & Security Commitments
Every engagement follows the same core standards, regardless of project size or scope.
Data Privacy Compliance
All systems designed in accordance with the Malaysian Personal Data Protection Act (PDPA) requirements.
Documentation Standards
Comprehensive architecture documentation and runbooks delivered with every project for long-term maintainability.
Iterative Reviews
Weekly review cycles keep projects aligned with your evolving requirements and prevent scope drift.
Access Control
Role-based access, encryption at rest and in transit, and audit logging are standard in every architecture.
What Drives Our Work
At novusynars, we operate from a core belief that AI outcomes are only as good as the data systems supporting them. Organisations invest heavily in model development and talent acquisition, yet often overlook the infrastructure layer — the pipelines, feature stores, and governance frameworks that determine whether models perform reliably outside of a notebook environment.
Our team brings hands-on experience across cloud-native and hybrid environments, having worked with data ecosystems ranging from early-stage startups to established enterprises. We understand the practical constraints of real-world data: inconsistent schemas, evolving sources, regulatory requirements, and the constant pressure to move faster.
We take pride in writing clean, well-tested infrastructure code and producing documentation that your team will actually use. Our engagements are designed to leave your organisation more capable, not more dependent — because the best data partner is one that empowers your internal team to own the system long after the project concludes.
Interested in Working Together?
We would be glad to hear about your data challenges and explore how we might help. No pressure — just an honest conversation about where you are and what might come next.
Reach Out to Us