What AI Readiness Actually Means, and How to Measure It
Your organisation is AI-ready when adding or changing an AI feature is routine, reviewable and reversible. Six areas to measure it, with the evidence to look for.
Read the article →Insights
Field notes from production AI.
Architecture write-ups for CTOs and engineering leads. Every article is also published as anOpen Knowledge Format (OKF) v0.2 document, so your agents can read it with its metadata intact.
A Paved Road for AI Workloads on Azure
The Azure platform defaults we set before a team ships its first AI feature: managed identity, private model endpoints, an AI gateway, tracing, cost attribution and eval gates.
Read the article →Agentic Workflows in Regulated Fintech: Guardrails That Survive an Audit
How to put AI agents into payments, collections and lending workflows without creating reportable incidents: propose-approve, idempotency, decision records and residency.
Read the article →Designing an MCP Server Agents Cannot Misuse: Lessons from FAQ Hub
What we learned exposing a shared SaaS platform to AI agents over the Model Context Protocol: scoped keys, plan-then-apply tools, explicit publish and a gap feedback loop.
Read the article →Keeping Customer Data Separate When AI Agents Call Your Systems
Shared SaaS platforms were built for apps that only ask for their own customer's data. AI agents write their own requests. Five patterns that keep each customer's data private.
Read the article →Find out what is stopping your AI from reaching production.
In two to three weeks, our AI readiness assessment gives you a score across six areas, your risks ranked by impact, a target architecture and a costed 90-day roadmap.