AI

The AI-Ready Enterprise: Preparing Data for the Age of AI

A practical framework for building data systems that AI can find, understand, trust, and use.Conversations around enterprise AI are moving rapidly from models to applications. Foundational models are becoming increasingly capable, and organisations are experimenting with retrieval augmented generation (RAG), tool calling, AI agents and multi-agent systems to bring intelligence into their business processes. Yet, as these systems move closer to production, a familiar constraint emerges: the data they need was rarely designed for AI consumption. Enterprise data has traditionally been...

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Engineering the AI Partnership: Making AI-Accelerated Delivery Reliable

We rebuilt an existing website to be CMS-driven: Strapi owned content and page structure, while a modularised Vue frontend handled rendering and behaviour. Over an 8‑week delivery window, we went from “AI is exciting but unpredictable” to shipping — by treating AI like a teammate with a clear process, not a magic button. This post is a standalone case study of that evolution: the workflow we designed, the mistakes we hit early, and the system that kept . When we first got...

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From Naive Automation to Intentional Collaboration with AI

When Claude Code landed in our team, we dived right in and jumped on the AI train: we asked it to rebuild the site end-to-end with a CMS behind it. We had to recreate in with , so felt like the obvious move.It produced code fast — and we spent the next two days discovering why “fast output” isn’t the same as a system you can extend, test, and hand over. This post is the story of that failure, and the pivot that...

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AI in Technical Decision-Making: A Field Guide

We had to run discovery, to do it, and to deliver — so instead of debating CMS options in theory, we used AI to and let the client make an evidence-based choice.We were brought into a project to migrate parts of an existing web application into a Content Management System (CMS) so the marketing team could ship content without constant developer involvement.The constraint that shaped everything: we had less than a week to complete discovery (including presenting working prototypes), and then eight...

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What Happens As You Code with AI? Beyond Vibe Coding

Discover how to move beyond rapid AI-generated code prototypes and build production-grade softwareLearn techniques for using Model Context Protocols (MCPs) and external knowledge to provide meaningful context to AIUnderstand methods for effectively reading, reviewing, and refactoring AI-generated codeEnsure maintainability, reliability, and security in AI-driven development workflowsTransform rapid code generation into a disciplined engineering practice with AI assistance

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What Happens Before and After the Code? AI Has a Role There Too!

Explore how AI can support software engineers throughout the development lifecycle, from requirements gathering and system design to post-release debugging and troubleshooting.Learn practical prompting techniques, real-world workflows, and strategies to leverage AI tools like Copilot and Claude Code more effectively.Discover the role of AI in improving test strategies, code quality, requirement gathering, system design, and post-release maintenance.Understand the differences between few-shot and one-shot prompting, and when to use each approach.Gain insights into prompt engineering, context engineering, and chain-of-thought prompting for...

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Latency Diet for Large Language Models: Cutting Millisecond Fat Without Losing IQ

When I first started building my own model (a language model for my need ), I went through a lot of videos and built it from scratch. While doing so I noticed something interesting: A model that answers in 300ms feels brilliant, while the same model taking 3 seconds feels broken, even if the answers are identical. Of course, not all extra time is wasted. With reasoning models, those additional seconds often reflect deeper “thinking” running through more steps, generating...

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Using AI to Simplify Developer Onboarding

Today, the market is filled with LLM-powered tools, with new and more advanced options emerging almost daily. In this article, we focus on one use case: integrating AI into the software development process to simplify onboarding for new developers.Onboarding a new developer involves a mix of tasks for both the newcomer and the existing team, often making the process hard to standardise or automate. Early days typically include reading documentation, meeting the team, attending sessions, and waiting for access to...

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My Journey With Cursor AI: More Than Just Code Completion

If you've been coding for a while, you've probably tried dozens of IDEs and editors. Each new tool promises to be the silver bullet that solves all your development problems. Most fall short.When I first heard about Cursor AI, I was skeptical. "Great, another VS Code clone with some fancy autocomplete," is what I thought. I couldn't have been more wrong.My introduction to Cursor was like learning to drive an F1 car after years of riding a bicycle. The power...

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Cursor AI: A Game-changer in Sahaj's Dev Process

In the constantly changing software development landscape, the use of AI tools is a game-changer. Sahaj's team was tasked to port an Angular codebase to React in an efficient manner without sacrificing quality. While migrating this, we strive to leverage generative AI tools to accelerate the development process and reduce manual effort, which will assist in streamlining tasks and increasing efficiency and productivity during this. We tried out a few options like GitHub Copilot and Bolt, we landed on Cursor...

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