Flagship · live service
demo.appliediqsolutions.comConfirmation Outlook
Which orders miss their date next week — measured, not modeled.
A supply-chain risk service for a fictional manufacturer, built end to end — Python engine, real API, operator console. Every probability is a measured frequency over observed week-to-week transitions: no model is fitted, and none is claimed. The engine is scored against a week it was never shown.
- 2,400
- materials
- 12
- DCs
- 53k
- order lines
- 100%
- synthetic
/ 01 How it’s built
The restraint is the point.
Anyone can bolt a model onto a dashboard and call the output a forecast. This one only ever tells you what it actually measured — and refuses to ship if the numbers don’t hold.
The image builds itself.
The container generates its own world, scores the engine against it, and checks 42 assertions. One red assertion and the image never ships.
One process, three front doors.
One service answers the REST API, an MCP server AI clients can drive, and the React console — all reading the same validated numbers.
The AI is on a leash.
Answers come only from what the engine computed — rate-limited per visitor, capped by a hard daily spend ceiling.
Validated output served live · warm state baked at build
This is what I ship. Yours would be built the same way.
Built end to end for a fictional manufacturer — engine, API, and console. Tell me the problem your operation is actually carrying, and I’ll tell you straight how I’d build the fix.