Evidence-led coverage of artificial intelligence, models, data tools and responsible deployment.
AI ROI measurement that survives scrutiny: capture a baseline before deployment, measure one workflow, count review labor, report ranges not multiples.
An AI data quality checklist of six checks — coverage, labels, duplicates, leakage, freshness, provenance. Why leakage flatters and labels cap you.
The best AI assistant for teams is decided by data terms, admin controls and week-two usage — not benchmarks. How to run a trial that tells you something.
Local AI vs cloud AI on four axes — data residency, capability, cost shape and upkeep. Why inflated GPU memory prices moved the cost crossover.
What is an NPU and what does it change? Why efficiency per watt matters more than TOPS, and why it will not run large local models.
How to reduce AI hallucinations with workflow, not model shopping. Supply sources, require citations, allow refusal, and verify where errors cost most.
Fine tuning vs RAG vs prompting matched to symptoms. Why retrieval fixes knowledge gaps, fine-tuning fixes behavior, and prompting comes first.
RAG explained as what it is — a search problem with a model attached. The four steps, where it breaks, and how to evaluate retrieval separately.
A careful Ollama setup for macOS, Windows or Linux, with model selection, local API checks, data boundaries and removal steps.
A source-based comparison of how OpenAI, Google and Anthropic position their 2026 models, plus the tests buyers should run before choosing.
