Building Software Confidence Through Careful Testing
We're a Petaling Jaya-based team focused on bringing structured, AI-informed quality assurance to Malaysian software organisations.
Back to HomeWhere Neuraplex Began
Neuraplex grew out of a straightforward observation: many Malaysian software teams were shipping code with incomplete testing coverage — not out of negligence, but because building and maintaining comprehensive test suites demanded significant time and specialist knowledge that most engineering teams simply didn't have available.
Founded in Petaling Jaya in 2019, we started by helping early-stage product companies in the Klang Valley set up their first structured test pipelines. Over time, our work evolved towards applying machine learning to the testing process itself — using AI to make coverage decisions smarter rather than just faster.
Today, we work with product teams, digital agencies, and enterprise IT departments across Malaysia — providing test automation, visual regression systems, and quality analytics tailored to each team's actual working environment.
Mission
To help Malaysian software teams ship with greater confidence — by making quality assurance more systematic, more data-informed, and less burdensome to maintain over time.
Values
- Transparency — we explain our methods clearly so teams understand what's being tested and why.
- Rigour — we follow structured processes rather than improvising, because consistent methodology produces dependable results.
- Collaboration — quality is a shared responsibility, and we work with your engineers rather than alongside them at a distance.
6+
years active
120+
engagements
94%
satisfied clients
People Behind the Work
A compact team with deep focus on software quality, machine learning, and development tooling.
Arif Hakim
Founder & Lead QA Architect
Over a decade in software quality engineering, with a focus on bringing machine learning into test design and coverage strategy.
Nurul Liyana
AI & Automation Engineer
Builds and maintains the AI test generation models and handles CI/CD integration work across client environments.
Zulaikha Razak
Quality Analytics Lead
Specialises in defect prediction modelling, risk dashboard development, and interpreting quality metrics for development teams.
How We Approach Quality
The protocols we follow across all engagements — regardless of project size or service type.
Documented Test Plans
Every engagement begins with a written test plan — scope, entry and exit criteria, risk areas — reviewed and agreed with the client before work begins.
Data Confidentiality
Client codebases, data, and system access are handled under NDA and strict access controls. No client data is used beyond its agreed purpose or retained after project completion.
Version-Controlled Artefacts
All test scripts, configurations, and models are maintained in version-controlled repositories — giving your team full visibility and portability after handover.
Peer Review Process
No test framework or model goes to a client environment without internal peer review — reducing configuration errors before they reach your pipeline.
Knowledge Transfer
Each engagement closes with a structured handover session — documentation, walkthrough, and a question period — so your team can maintain and extend what we've built together.
Outcome Reporting
We produce clear, readable reports throughout the engagement — covering coverage metrics, defect findings, and test run summaries — presented in accessible language for both technical and non-technical stakeholders.
AI-Informed Software Testing in Malaysia
Software quality assurance has historically been a labour-intensive discipline — one that expanded proportionally with product complexity and team size. For many Malaysian engineering teams, particularly those building SaaS products, enterprise platforms, and consumer applications, keeping test coverage meaningful without overwhelming QA resources has been a persistent challenge.
Neuraplex addresses this through the application of machine learning to the testing process itself. Rather than simply automating scripted interactions, our approach uses AI to analyse code change patterns, identify regression-prone areas, and make smarter decisions about where testing effort should be concentrated at any given moment.
Visual quality is another dimension that standard functional testing often overlooks. As teams iterate on UI components and design systems, pixel-level regressions can introduce inconsistencies that affect user trust — particularly in fintech, e-commerce, and enterprise dashboards where interface predictability matters. Our visual regression service uses computer vision to detect and flag these changes before they reach end users.
The third pillar of our work — defect prediction analytics — moves quality assurance upstream, into the planning process. By modelling relationships between code complexity, commit patterns, and historical defect rates, we help teams anticipate which areas of an upcoming release carry elevated quality risk — so that attention can be directed where it's most needed before testing even begins.
Interested in Working Together?
We're straightforward to talk to. No hard sell — just an honest conversation about what your team needs.
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