PangeaAIR quantitatively assesses AI systems across five dimensions of reliability — then goes further, retraining models to make them more reliable without sacrificing accuracy.
In safety-critical work, accuracy alone is not enough.
A model can score well on a benchmark and still be brittle — vulnerable to manipulation, inconsistent under pressure, unexplainable to a regulator, or unable to generalize.
PangeaAIR measures what benchmarks miss, and gives regulated industries an independent, quantitative basis for trust.
Before a model is ever deployed, PangeaAIR computes a quantitative reliability score for each dimension — an independent, numeric assessment of how reliable the model actually is, produced prior to deployment rather than discovered in production.
We do not just measure reliability — we improve it.
PangeaAIR closes the loop: where a dimension scores weak, we can further train the model to make it more reliable — without sacrificing accuracy. It is the independent audit and improvement infrastructure regulated industries need.
Both the score and the improvement are powered by our own advanced, high-dimensional geometry — proprietary, patent-pending methods developed at Pangea.