Insights / Enterprise AI architecture
Clear, experience-led articles on enterprise AI architecture, secure RAG, bounded agents, and the decisions teams need to make before production.
01 / Start here
Each article turns one production concern into a reviewable set of boundaries, questions, and next decisions.
01 / Enterprise AI architecture
A practical enterprise AI architecture framework covering five runtime layers, three control planes, and the questions that expose production gaps.
Read insight02 / Secure RAG
Design secure, identity-aware RAG with authorization before retrieval, evidence lineage, safe citations, abstention, and separate retrieval evaluation.
Read insight03 / Agentic AI architecture
A practical bounded-autonomy framework for AI agents: goals, scope, authority, budgets, invariants, stop conditions, and evidence before expansion.
Read insight04 / Architecture review
A practical 12-question AI architecture review checklist for business fit, data, RAG, agent authority, security, evaluation, operations, and ownership.
Read insight02 / The point of view
Ardevant starts with the outcome, authority, data, risks, and operating model. Products and models are chosen only after the system responsibilities are clear.
The articles are drawn from Anton's course methods and focus on decisions a team can document, test, and defend in an architecture review.
No platform is presented as a shortcut around authorization, evaluation, security, or accountable ownership.
03 / Put it into practice
Explore the public courses in Zürich, arrange private training for a team, or bring a consequential design to an independent review.