AI/ML

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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Prompting Is Architecture: Why Understanding Systems Matters More Than Ever

A Spark story about scale, grain, and why speed makes system awareness non-negotiable.Image generated using ChatGPTRecently, I ran into a Spark job that looked perfectly fine. I used Claude in this case to generate code, which wasSyntactically correctSemantically correctEasy to read and reason aboutThis worked on the sample dataset. Assuming Spark will manage the scale, we pushed it to production. However, on prod, it took more than 3 hours to complete on a 40-node EMR cluster. It was trying to...

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Delivering a 40% Velocity Boost in the Modernization of Talon Plato's Legacy System with GenAI

Sahaj Software modernized Talon's Media Owner Portal from legacy Angular 17 to a contemporary React architecture. This initiative went beyond a simple rewrite, piloting "intelligent Engineering", an AI-augmented, Gen-AI-powered workflow. This approach effectively managed evolving UX needs and technical debt, ensuring predictable delivery and establishing a scalable modernization blueprint, which positions Talon as a digital innovation leader in the OOH space.Talon is a specialist Out-of-Home (OOH) agency with over 30% UK market share and operations in the UK, USA, Canada,...

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Project xEMR: Building an AI-Powered EMR System with Agentic AI Workflows

Project xEMR demonstrates the successful use of advanced, automated AI systems to build a complex medical records platform that strictly adheres to all healthcare regulations. The intelligent Engineering approach fundamentally transformed the software development lifecycle by having AI agents handle 90% of the work, resulting in a 300% increase in engineer productivity.October 2025The healthcare industry is bogged down with complex, time-consuming paperwork and tasks, and often the standard Electronic Medical Records (EMR) systems are part of the problem. That's why...

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Driving Success of AI Initiatives Using the Power of Systems Thinking

The key to scaling AI from proof-of-concept to enterprise success in B2B is adopting systems thinking, which integrates the right user experience, value definition, and complementary non-AI features into the solution.In today’s tech landscape, Artificial Intelligence is often presented as the ultimate solution to every challenge. This can lead one to experience the ‘Law of the Instrument’ phenomenon. When AI is your powerful new hammer, you start seeing every problem as a nail. We rush to apply complex models and...

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Building an AI-Powered EMR Extension with Agentic AI Workflows

The healthcare industry is bogged down with complex, time-consuming paperwork and tasks, and often the standard Electronic Medical Records (EMR) systems are part of the problem. That's why we embarked on Project xEMR to build an AI-augmented EMR Extension platform. The purpose was to dramatically cut the administrative work for medical staff. Project xEMR is an AI layer that sits on top of existing EMR systems. It doesn't replace them; it only makes them smarter. It strictly follows essential healthcare...

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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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Harnessing Agentic AI to Drive Competitive Advantage

Our global Emerge series hosted an invitation-only roundtable in New York on September 17th, 2025. Representatives from a variety of industries including financial services, B2B commerce, cloud services, social media technology, and community and social services gathered to discuss “Harnessing the Value of Agentic AI to Drive Competitive Advantage.” The discussion compared agentic AI with programmatic workflows, described a strong agent, and agreed on important points and next steps.AI agents are goal-seeking loops, not fixed scripts. An agent plans a...

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