Skip to content
AI · Nov 11, 2026 · 4 min readFrom Idea to AI Feature in Six WeeksHow an AI project runs end to end in six weeks: discovery, design, prototype, infrastructure, rollout, then measuring and improving.#automation#Infrastructure#LLM
AI · Nov 2, 2026 · 4 min readCI/CD for Models and PromptsEvaluation gates, model registries, canary releases and rollback: applying CI/CD discipline to prompts and models in LLM applications.#CI/CD#devops#Kubernetes
AI · Oct 23, 2026 · 4 min readFine-Tuning or Prompting? A Decision Guide for Small TeamsWhen prompting, RAG or fine-tuning is the right way to adapt a language model, and what each one costs a small engineering team.#Fine-Tuning#LLM#MLOps
AI · Oct 15, 2026 · 4 min readAutomating Business Workflows with AI, SafelyWhere AI fits in business process automation, and how human review, guardrails and audit trails keep automated workflows safe.#AI Agents#automation#Business Process
AI · Oct 6, 2026 · 4 min readObservability for LLM ApplicationsWhat to measure in an LLM application: tokens, latency, cost per request, OpenTelemetry traces and the quality signals that show when answers get worse.#Grafana#LLM#Monitoring
AI · Sep 25, 2026 · 4 min readHosted LLM APIs or Self-Hosted Open Models?GPT, Claude and Gemini APIs against self-hosted open models, compared on cost, privacy, latency and the engineering effort each one needs.#cloud#Cost Optimization#LLM
AI · Sep 16, 2026 · 4 min readRAG in Production: The Boring Parts That MatterIngestion, chunking, refresh, evaluation and access control: the unglamorous work that decides whether a RAG system can be trusted.#automation#Infrastructure#LLM
AI · Aug 27, 2026 · 4 min readvLLM vs Ollama vs TGI: Choosing an Inference ServerHow vLLM, Ollama and Hugging Face TGI differ in throughput, hardware support and operations, and which one fits your workload.#Infrastructure#Kubernetes#LLM
AI · Aug 18, 2026 · 4 min readRunning Open Models in Production: A Practical Starting PointWhat it takes to run Llama, Mistral, Qwen, Gemma or DeepSeek in production: picking a model, sizing hardware, serving it and keeping it healthy.#GPU#Infrastructure#Kubernetes
Page load link
