Applied AI insights
Field notes on RAG, agents, data readiness and responsible delivery for teams moving from prototype to production.
Featured insight
Retrieval · evaluation · accountable action
The most common gaps between an impressive demonstration and a reliable knowledge system used by real teams.
Read featured insightApplied AI playbook
Focused guides covering RAG, agents, data readiness, model choice, cost, architecture and responsible operation.
A practical governance model for teams that need control without slowing every experiment.
Read articleThe operating practices that keep LLM applications reliable as models, data and user behaviour change.
Read articleHow to make human review meaningful, timely and accountable instead of a decorative approval step.
Read articleWhat changes when a proof of concept must become a secure, observable and supportable business system.
Read articleA field guide to ownership, quality, access and integration before building an AI workflow.
Read articleA release ready evaluation model for retrieval relevance, grounded answers, safety, latency and cost.
Read articleHow to choose between deterministic workflows and bounded agentic behaviour.
Read articleThe architecture and operating practices that keep enterprise assistants useful after launch.
Read articleA decision framework for choosing retrieval, fine tuning or a combined design without overbuilding.
Read articleA practical readiness test for deciding whether AI will improve a workflow or simply add complexity.
Read articleLatest thinking
New perspectives on product, engineering and the decisions that turn good ideas into dependable customer outcomes.
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