Software Architecture

Vector Databases Explained: How AI Finds Meaning in Data

Subtitle: Understanding embeddings, similarity search, metadata filtering, and the architecture behind modern AI applications. When building an AI application, one of the first challenges is finding the right information from a large collection of documents. Imagine an engineering organisation with thousands of technical manuals, design reports, research papers, and troubleshooting records. A user asks: “How…

RAG Explained: How to Give AI Access to Your Own Knowledge

Subtitle: Understanding Retrieval-Augmented Generation, from document processing and vector search to reliable, source-grounded answers. One of the common challenges when building an AI application is helping it answer questions about information that is specific to a company, project, or technical domain. A general-purpose language model may understand programming concepts, software architecture, and engineering terminology. But…

AI and Data Privacy: Building Intelligent Systems Without Losing Control of Your Data

Subtitle: Practical architecture principles for protecting business information in AI-powered applications. Artificial intelligence becomes much more useful when it can work with real business information. An AI assistant connected to technical documentation can help engineers find answers faster. A system that understands company procedures can assist employees with routine questions. An application connected to business…

When AI Gets It Wrong: Designing Systems That Fail Safely

Why reliable AI applications need validation, recovery mechanisms, and clear boundaries—not just intelligent models. Artificial intelligence is becoming part of everyday software engineering. We are using AI to analyze documents, answer technical questions, generate code, summarize information, and automate business processes. The capabilities are impressive. But there is an important engineering question that deserves equal…