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For twenty years, every software company sold you the same deal: you don’t own it, you rent it. Multi-tenant architecture, a generic feature set built for the average customer, and a recurring invoice that lands whether you used the product or not. That deal made sense for exactly one reason building custom software was […]
For twenty years, every software company sold you the same deal: you don’t own it, you rent it. Multi-tenant architecture, a generic feature set built for the average customer, and a recurring invoice that lands whether you used the product or not. That deal made sense for exactly one reason building custom software was expensive,
The SaaS Model Just Died. Nobody’s Sent the Obituary Yet. Read More »
A company can have access to the latest AI models, dozens of AI tools, and a growing list of automation ideas and still make very little progress. The problem is usually not the technology. It is knowing where AI should be applied, what needs to change around it, and how to move from promising
What Is AI Transformation Consulting? A Complete Enterprise Guide Read More »
Introduction Recently, I worked on integrating an RFID reader into a React application. Although I’ve integrated APIs and third-party libraries many times before, working with hardware devices was a completely different experience. It gave me an opportunity to understand how browsers communicate with physical devices and how browser APIs can be used beyond traditional web
Integrating an RFID Reader into a React Application Using the Web Serial API 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 »
