Every system inside aimlcompanion.ai in production

A case study of one specific product, not a universal template, built by me over the last 5+ months (including content curation) and still iterating. The full stack running aimlcompanion.ai today, the larger-scale upgrades I have not made, and an honest take on which of those upgrades would be worth it for this product versus which would only slow shipping.

A case study of aimlcompanion.ai specifically, not a template for every production app. The visible product of a content-heavy learning platform is the modules, the visualizations, the narrated walkthroughs, the ML mini-games, and the progress analytics. The invisible product is the production stack wrapping all of that, made up of caching, auth, deploys, observability, and the layered safeguards that turn a working app into a service real users keep paying for. This post walks through every one of those layers as it runs behind one live AI and ML learning platform, with a clear-eyed look at where larger-scale engineering would add value and where it would only slow shipping.