Blog

The Vibe-Coded Trap: Fast Demos, Slow ROI — and how to avoid it

Beyond Body Shopping: How AI-Augmented Talent integrates into your roadmap

Architecting for Scale: Why your AI strategy is only as good as your Data Infrastructure

There is a pattern we see repeatedly in AI engagements: a company invests heavily in model development — hiring data scientists, experimenting with LLMs, running pilots — and then hits a wall. The models work in isolation. They fail at scale. The culprit, almost without exception, is the data infrastructure underneath. AI is not a layer you add on top of your existing systems. It is a capability that emerges from the quality, accessibility, and reliability of your data foundation. This article explains what that foundation looks like, why it matters, and how to build it in a way that supports both current AI initiatives and the ones you have not thought of yet.

From PoC to Production: A Practical Playbook for shipping AI projects that actually Go Live

Most AI initiatives die as over‑engineered proofs of concept that never reach production. This article lays out a pragmatic, battle‑tested playbook for taking AI from idea to a reliable, maintainable system in production.