AI is changing how technical work gets done. But in engineering, manufacturing, and industrial software, the real challenge is knowing how to apply the tools responsibly, safely, and commercially.
This site is for engineers, software developers, product leaders, and business decision makers who are trying to understand where AI and modern software genuinely help — and where they create new risks, distractions, or false confidence.
Here you’ll find practical thinking on:
The focus is practical: better judgement, stronger systems thinking, responsible AI adoption, and technology strategy grounded in industrial reality.
I share my experience about how AI, industrial software, and digital systems can be applied in manufacturing and engineering environments — without losing the judgement, context, and discipline that technical work demands.
Industrial AI, automation, agents, data, and decision support — viewed through the lens of real plants, real constraints, and real operational risk.
Articles on strategy, software products, business ownership, decision-making, and leading technical people through change.
Join me as I build apLabs, an AI powered knowledge management toolkit and productivity accelerator for my business.
I am a chemical engineer, former CIO, software company co-founder, and technology strategist with more than 30 years of experience across process engineering, industrial software, IT leadership, product strategy, and business development.
I enjoy sharing my thoughts about AI and technology. I am interested in practical application, not hype — especially in environments where reliability, safety, judgement, and business value matter.
My investigation into Obsidian began with knowledge graphs and the idea of a personal “digital brain”. For consulting teams the broader concept is often oversold. For consultants, retrieval and practical context matter more than a beautiful graph.
apLabs2 implements RAG using a self-hosted MongoDB Community Edition database by storing embeddings as standard data and performing similarity calculations within C# code. The system employs hybrid retrieval combining semantic ranking via OpenAI’s text-embedding-3-small with lexical matching, utilizing Reciprocal Rank Fusion to blend results while maintaining operational efficiency at current scale.
Microsoft Build is once again focused on AI agents, Copilot and the emerging concept of the “Frontier Firm”. While the technology is impressive, two practical concerns remain: unpredictable AI pricing and Microsoft’s increasingly confusing Copilot branding. After a weekend experimenting with Codex and researching self-hosted AI alternatives, I’m convinced that affordability and clarity may prove just as important as model capability in determining how quickly businesses adopt agentic AI.
AI investment is moving fast, but many enterprises are building fragile ecosystems underneath the hype. CIOs are now facing tougher questions around ROI, vendor lock-in, governance, and rising operational costs. This article explores how enterprise leaders can build a resilient AI strategy that survives changing market conditions by focusing on procurement discipline, modular architecture, measurable business outcomes, and strong governance. It outlines practical steps to reduce dependency risk, control hidden AI costs, and keep AI investments tied to real operational value rather than experimentation alone.
MongoDB helped apLabs get started, but the real lesson came later. Atlas Free Tier was a generous way to learn the platform and build iteratively, but as the project grew, performance on the hosted free tier became the bottleneck. Moving to self-hosted MongoDB Community Edition on a local Debian server solved the immediate constraint and kept the work moving without forcing an unnecessary upgrade.
Typing is not always the best way to think. In apLabs, I can record rough spoken notes, convert them to text automatically, and use an LLM to turn them into a structured first draft for a blog article in minutes rather than hours.