All experiments

Systems In progress

AI Commerce Engine

How much of a digital-product workflow can be made systematic, reproducible and automatically testable?

Pipeline diagram: a brief and its sources produce a master document, which is rendered into per-profile exports and previews. Eight families of quality gates — evidence, learner fit, learning value, agency, accessibility, culture and ethics, media and commercial — are checked against a SHA-256 of the product, profile or release. A manifest records content-addressed exports, and a marketplace package is released only after an explicit human approval.
Dry-run is the default: nothing paid or networked happens without an explicit approval, and a verdict only enters the record bound to a hash.

Put the whole thing behind gates: generation, then export into multiple formats, then automated quality checks, then a manifest recording which inputs and which pipeline version produced which outputs.

The generative step turned out to be the least difficult part. Export correctness, validation and reproducibility took the real work.

A pipeline where nothing ever fails a check turned out to be a warning sign rather than a success: it meant the checks were not testing anything.

Which failures can only be caught by a person, and how should the pipeline route those rather than pretend to decide them?