Finish what the interface started.
Practical systems for the work AI-assisted builds often leave implicit: architecture, identity, access, data, billing, security and production verification.
How to make an AI-built app production-ready
A practical system for finding the access, data, security, billing, privacy and operational work that sits behind a finished-looking interface.
How to use ChatGPT Projects for full-stack development
Structure project files and instructions so ChatGPT can reason across architecture, implementation, security and release instead of treating every chat as an isolated request.
How to prevent missing RLS in AI-generated apps
A concrete method for turning tenant and ownership requirements into database-enforced row-level policies, tests and service-role boundaries.
Cursor rules for authentication and billing work
How to encode durable access, payment and verification constraints in repository-scoped Cursor rules without turning one rule file into a vague prompt dump.
A production-readiness checklist for Lovable builds
What to inspect after the interface works: account recovery, authorization, tenant isolation, server integrations, privacy, failure states and release evidence.
Why AI-generated SaaS fails after the interface
The interface is the easiest layer to demonstrate. The product usually fails where identity, state ownership, authorization, recovery and operations meet.
How to give AI the context needed to build a complete product
Replace the oversized master prompt with a layered context system covering product facts, architecture, specialist knowledge, current work and verification evidence.
Built from implementation boundaries, not search volume.
Each guide starts from a real production question, separates verified behavior from assumptions and links the problem to the specialist systems that govern it. Pages are reviewed when platform behavior or Advea’s own implementation evidence changes.