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TrinolIT
TrinolIT

AI systems that stay inside their limits

Retrieval scoped to the data the user is allowed to see, tools the model selects from rather than composes, and a grounding check that every figure it states came from a real result.

01 — The problem

What has to stop going wrong.

You want an assistant over your own data, and you cannot have it inventing figures or reading records the user is not allowed to see.

02 — What we build

The system behind the surface.

Retrieval scoped to the caller's permissions, a whitelist of tools the model selects from rather than composes, and a grounding check that every number it states appeared in a real result. The model never writes a query.

  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.

An assistant that answers from a business's own records and cannot state a figure that is not in them.

Shown in Nexora AIRetail and trading

View the case study →