A business decision guide to RAG, fine-tuning and long context windows: what each one actually changes, how the costs compare at real query volumes, a six-question scoring matrix, the hybrid patterns mature teams use, and the governance obligations each option creates.
A practical operating model for autonomous AI agents: named ownership, four proportionate approval gates, risk tiering, monitoring thresholds that trigger re-approval, the five artefacts it runs on, and a 90-day build sequence.
A practical AI incident response plan for teams running models and agents in production. Covers what counts as an AI incident, a six-phase response structure, a copyable severity matrix, containment options ranked by blast-radius reduction, non-deterministic root cause analysis, disclosure duties, and a thirty-day build plan.
A practical reference for the four families of AI agent evaluation metrics: accuracy, cost, safety and reliability. Includes computable definitions, starter thresholds, evaluation-set design, LLM-as-judge validation, and how to wire the numbers into a release gate.
A practical guide to monitoring AI agents in production: the four signal groups that matter, run tracing, quality drift detection, prompt-injection and tool-abuse signals, cost per successful outcome, and a staged 90-day rollout.
AI system inventory work starts with an uncomfortable question nobody can answer: which AI tools are we actually using? This guide gives you the register that answers it — a field-by-field template, the five places shadow AI genuinely hides, seven discovery methods that surface tools nobody declared, a two-week sweep plan, a four-tier triage scale, and a clean mapping to ISO/IEC 42001, the NIST AI RMF and the EU AI Act.
AI risk assessment is the step most organisations skip between “this tool looks useful” and “forty people now depend on it”. This guide gives you a two-page template you can fill in section by section, the seven risk categories worth scoring, a likelihood-impact-exposure scale that produces comparable numbers, the six steps of the exercise itself, the five controls that genuinely move a score, and a clean mapping to the NIST AI RMF, ISO/IEC 42001 and the EU AI Act.
AI vendor lock-in is the bill that arrives eighteen months after a successful pilot. This guide breaks dependency into five distinct types — model, data, workflow, integration and commercial — explains why AI switching costs behave differently from classic software migrations, sets out the contract clauses that cap your exposure at signature, covers the architecture decisions that keep switching cheap, and gives you a four-page exit plan you can write in a day and rehearse once a year.
A practical playbook for testing AI agents before launch: build a real evaluation set, red-team prompt injection and tool abuse, prove reliability under load, and gate every release on objective checks.
A working checklist of 30 questions to ask any AI platform vendor before you sign, covering company viability, model provenance, data rights, security, pricing and exit terms.
