01 — The problem
What has to stop going wrong.
You need forecasting, detection or classification on your own data, and an off-the-shelf model does not know your categories.
02 — What we build
The system behind the surface.
Pipelines built against a named benchmark with per-class results, so the classes that fail are named rather than averaged into a headline. Deployment and monitoring included.
01
Bound it
Define the failure modes, permissions and consistency rules before the interface hides them.
02
Build it
Ship production code in your repository, in phases with a written exit state.
03
Try to break it
Test the paths that carry money, identity, isolation and irreversible decisions.
03 — Decision material
Know whether it fits before a call.
A strong fit
- Teams with data and a prediction or detection decision
- Problems where weak categories matter more than one average
- Models that must leave a notebook and enter a product
What leaves with you
- Dataset and label-quality audit
- Reproducible training and evaluation pipeline
- Per-class error analysis and operating threshold
- Deployment and drift-monitoring plan
04 — Evidence
A claim should lead somewhere.
Stock-out warnings and demand trends surfaced before they become a problem.
Shown in Nexora AI — Retail and trading