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TrinolIT
TrinolIT

Machine learning and computer vision

Forecasting, anomaly detection and detection pipelines built against a named benchmark, reported per class rather than behind a single headline score.

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.

  1. 01

    Bound it

    Define the failure modes, permissions and consistency rules before the interface hides them.

  2. 02

    Build it

    Ship production code in your repository, in phases with a written exit state.

  3. 03

    Try to break it

    Test the paths that carry money, identity, isolation and irreversible decisions.

03 — Evidence

A claim should lead somewhere.

Stock-out warnings and demand trends surfaced before they become a problem.

Shown in Nexora AIRetail and trading

View the case study →