Grounding
Tying outputs to something checkable — sources, constraints, structured state — so that a fluent answer and a correct one stop being the same shape.
Research
We work on the layer around the model
Most of what determines whether a deep learning application can be trusted sits outside the weights — in what the model is given, what is checked before its output is used, and how intent is expressed in the first place. That layer is what we study.
Detecting deviation
Knowing when a model has drifted from the expected output, cheaply enough to run on every call rather than in a post-hoc audit.
Explanation
Recovering why a model produced what it did, in terms the person responsible for the decision can actually act on.
Alignment to intent
Turning a stated intention into something the system enforces, so the application built on the model behaves the way it was specified to.
How we work
Whatever the failure actually needs
Sometimes the answer is fine-tuning. More often it is what surrounds the model: grounding it in retrieved context, catching drift before an output is used, making an intention explicit enough to enforce. We work across all of it, because the failure is rarely where the model ends.