Software Development Blog

Legacy modernization is not optional today. Without it, your business won’t be competitive, especially in this economy where every saved cent matters.

We may live in a world where technology has already surpassed many sci-fi movies, yet it has done little to advance healthcare workflow automation. Most clinics are stuck in a paper-and-phone era, and patients are paying the price.

If you’re playing with RAG just to impress your board, skip this one. If you want retrieval augmented generation to power products your users trust at 2 a.m., let’s talk.

The implementation of AI in software support and maintenance is revolutionizing businesses and, most importantly, their budgets. But of course, this tech can make a noticeable difference for you in this economy only when managed efficiently. That’s exactly what we are going to discuss here today.

You can build fast, you can build beautifully, but building the wrong thing still sinks the roadmap. And that raises the real question for any founder: why do strong engineering teams still ship features users ignore?

When you move from LLMs on slide decks to LLMs in production, LLM inference optimization stops being a nice-to-have and becomes your unit economics. A 2025 ACL study shows that proper LLM inference optimization techniques reduce energy usage by up to 73% compared to naive serving, which typically translates to a 2–3x reduction in cloud costs.​

What does a great TypeScript code review actually check? It answers one practical question: Does this update strengthen the system or introduce risk as it grows? A strong review examines how data shapes are modeled, how modules communicate, and how safely new logic moves through the architecture.

Integrating AI into legacy ERP systems is not a “nice-to-have” anymore; it is how you squeeze extra years of value from platforms you already paid millions for while unlocking ERP automation, predictive planning, and insight-based decisions without a full rewrite.

AI adoption in engineering teams is now standard practice. McKinsey (2025) reports that 88% of organizations use AI in at least one business function. In software development itself, the usage keeps on growing as companies integrate coding assistants and automated agents into their pipelines.

AI in real estate has become a central force in how deals progress and how teams stay organized. Brokerages and property management groups rely on CRMs to manage rising lead volumes, yet the same operational friction keeps slowing them down: scattered records, inconsistent lead scoring, and follow-ups that taper off as soon as workload increases.