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Here’s How We Helped a Global FMCG Brand Process 100,000+ Quality Testing Documents with AI Without Losing Accuracy When a global FMCG manufacturer came to us, the problem wasn’t a lack of data. It was too much of it, trapped in the wrong format. Every batch of product that left their manufacturing facilities came with […]
How We Helped a Global FMCG Brand Process 100,000+ Quality Documents with AI Read More »
Introduction As large language models move from proof-of-concept into production systems that touch real users, real money, and real decisions, the industry has been crying out for structured, actionable guidance on how to test them responsibly. IMDA’s Starter Kit for Testing LLM-Based Applications is a meaningful answer to that call. It arrives at exactly the
Beyond the Checklist: A Practitioner’s Review of IMDA’s LLM Testing Starter Kit Read More »
I’ve been in QA for a few years now. I know how testing works. You write a test case. You define the expected result. You run it. It either passes or fails. Simple. So when our team started working on an AI-powered feature, I thought, okay, same process. Different kind of input, but same idea.
Quality Assurance has always been about understanding risk and validating systems before they reach production. After more than eight years in QA and now working in AI security, governance, and red teaming, I often compare traditional testing practices with the challenges introduced by AI systems. While the risks have evolved from software defects to hallucinations,
Lessons from IMDA’s LLM Testing Starter Kit: An AI Assurance Perspective Read More »
I recently had the opportunity to review IMDA’s Starter Kit for Testing LLM-Based Applications for Safety and Reliability. As someone who has spent over 14 years in Quality Assurance, I was curious to see how established testing principles are being adapted to address the unique challenges introduced by Large Language Models (LLMs). What I expected
Breaking the Black Box: From Traditional QA to AI Assurance and Governance Read More »
Breaking the Black Box: From Traditional QA to AI Assurance and Governance I recently had the opportunity to review IMDA’s Starter Kit for Testing LLM-Based Applications for Safety and Reliability. As someone who has spent over 14 years in Quality Assurance, I was curious to see how established testing principles are being adapted to address
When the client first approached us with the idea of building an AI-driven preventive healthcare platform focused around cancer care and long-term wellness, the requirement initially sounded like a risk assessment system. But honestly, after the first few discussions, it became very clear that this was far bigger than a questionnaire, AI score generator, or
Building an AI-Driven Preventive Healthcare Ecosystem Not Just Another Healthcare App Read More »
Student retention has become a board-level metric for universities, bootcamps, and enterprise learning platforms. Yet many EduTech companies still struggle with fragmented LMS data, unreliable adaptive models, and FERPA compliance issues that slow releases and increase risk. This is where ADLC changes the conversation. An AI-driven software development lifecycle gives EduTech teams a structured framework
Predictive Analytics in EduTech Through an AI-Driven Software Development Lifecycle Read More »
The workplace has changed dramatically over the last few years. Remote and hybrid work models are now the standard for many organizations, giving employees the flexibility to work from anywhere. While this shift has improved productivity and employee satisfaction, it has also created major cybersecurity challenges. Businesses now face increasing risks from unsecured devices, weak
Endpoint Compliance in a Remote-First World: Why Businesses Can’t Ignore It in 2026 Read More »
Introduction One of the most common mistakes businesses make in IT service management is believing that a new tool will automatically solve operational problems. A platform gets replaced. New automation features are introduced. Dashboards become more advanced. Expectations rise quickly. But a few months later, support teams still struggle with delayed resolutions, inconsistent workflows, and
