Articles & Insights
Practical, technical writing on AI strategy, Generative & Agentic AI, RAG, data engineering, and graph databases.
The Skill You Can’t Skip Anymore — and Why You’re Not Too Late
Working fluently with AI has quietly become a baseline professional skill — not a specialism. The reassuring part: we have explored only a sliver of what these systems can do, so the people starting today are not behind. They are arriving exactly on time.
Read article →Beyond the Blueprint: Designing Data & AI Architectures That Outlast the Hype
The organisations that thrive are not those with the most sophisticated stack today, but those with the structural clarity to absorb the next disruption without starting over. A view across three dimensions: architecture, team capability, and execution discipline.
7 min readGraph Databases Explained — with Code and Downloadable Sample Data
A hands-on introduction to property-graph databases: the data model, when to use them over relational, and a complete worked example in Cypher with sample data you can download and load yourself.
12 min readBuilding Document Q&A with Any LLM: File Handling, Prompting, and Guardrails
A walkthrough of the engineering behind the live Document Intelligence demo: how files are handled, how the prompt enforces grounding, the guardrails that keep it safe — and why the LLM is swappable (Ollama, Llama, or cloud).
7 min readEvaluating LLM Output: Hallucination, Grounding, and Measurement
You cannot govern what you cannot measure. A practical framework for evaluating LLM outputs — distinguishing hallucination from grounding failure, and building evals that survive model changes.
8 min readThe Real Engineering Constraints of LLM Apps: Context, Token Cost, Latency, and Caching
LLM apps live and die on four constraints that rarely appear in demos: the context window, token economics, latency, and caching. Engineering for them is what makes an app shippable.
8 min readDesigning Agentic AI: Orchestration, Tool-Use, Memory, and Human-in-the-Loop
An agent is not just an LLM in a loop. Production agentic systems need bounded orchestration, typed tools, durable memory, and explicit human checkpoints. A practical architecture, with code.
9 min readWhy RAG Systems Fail in Production — and How to Engineer Them Properly
Most RAG prototypes demo well and break in production. The failure modes are predictable: weak chunking, naive retrieval, no reranking, and no evaluation. Here is how to engineer each layer, with code.
10 min readFrom Data Readiness to AI Readiness: Why Enterprises Need 3C and 3T
AI readiness is not only about models. It depends on content, context, compliance, talent, tools, and technology. A structured way to assess readiness before scaling AI adoption.
7 min readThe Physical Reality of AI: Why Sovereign AI Brings Architecture Back to the Metal
Enterprise AI is breaking the assumption of infinite cloud compute. Sovereign AI, local inference, latency, privacy, and cost are pulling infrastructure architecture back into strategic decisions.
7 min readPrompting vs Prompt Engineering: Why Production AI Needs Measurement
Most teams are prompting, not prompt engineering. The difference shows up the moment AI moves from human-supervised experiments to production workflows that need versioning, testing, and governance.
7 min read