AI Development Case Studies, and Insights
Reading time: 10 min
AI in Application Modernization: How It Works Under the Hood
The math on legacy application modernization has always been brutal. High cost, high risk, long timelines, and uncertain outcomes. According to a Pegasystems study, enterprises hemorrhage nearly $134 million each year on legacy transformation projects alone. A...
Reading time: 8 min
Manual vs. Automated Software Testing With AI Supercharges
With applications growing ever more intricate and release cadences tightening, development houses are constantly revisit...
Reading time: 9 min
Reinventing Project Management for AI-Driven Development
For years, we ran Time & Materials (TM) projects the way most experienced PMP-certified leaders do. We started with a draft scope. Our business analysts detailed each module, split it into features, and submitted tickets to Jira. Then the project manager a...
Reading time: 9 min
React vs Vue vs Svelte: Choosing the Best Frontend Framework in the Era of AI
AI coding agents have grown fluent in modern UI frameworks, and online services produce functional prototypes nearly eff...
Reading time: 10 min
How to Build a Local LLM Agent to Automate Work List Generation from Monthly Reports (With Jira Integration)
Our management team spent hours manually extracting work items (“bug fix”, “released version 1”, etc.) from doze...
Reading time: 7 min
Spec Kit on a Real Project: Implementation Experience in Large Legacy Code
Bringing new AI-powered development tools into a large, established legacy project is rarely straightforward. While conv...
Reading time: 8 min
AI Is Changing Disciplined Agile Delivery. Here’s How We’re Adapting
Before, we’ve relied on the Disciplined Agile Delivery (DAD) framework to bring structure, predictability, and scalability to our software projects. DAD has helped us balance agility with discipline, especially in complex, multi-team outsourcing environments...
Reading time: 11 min
The Gap Between AI Prototypes and Production Software: 10 Risks You Can’t Afford
We often meet founders who come to us with a familiar story. They have a compelling concept, a functional application ru...
Reading time: 8 min
Using AI to Generate a List of Works in Construction
Construction and infrastructure projects face growing data volumes, tighter deadlines, and increasing reporting inconsis...
Reading time: 9 min
How Spec-Driven Development Brings Structure to AI-Assisted Engineering and How We Put It to the Test
AI coding assistants have made developers incredibly fast since the start of the AI boom, but this new speed often comes...
Reading time: 12 min
General-Purpose AI vs. Medical AI: A Practical Guide for HealthTech Businesses
Nowadays, Artificial Intelligence is booming, but not every solution is interchangeable, especially in a clinical settin...
Reading time: 11 min
AI as a Co-Pilot, Not an Autopilot: Guidance on Risk Management and Realistic Performance
AI-assisted development — commonly called “vibe coding” — is already part of modern SaaS development. To...
Reading time: 9 min
AI MVP vs Traditional MVP: Key Differences, Benefits & Use Cases
Building a minimum viable product (MVP) is a core strategy for any startup or new product line. Decision-makers can choose between two flavors of MVP: a traditional MVP focused on core functionality, or an AI MVP that embeds artificial intelligence. AI helps a...