AI Reliability.

Prevent hallucinations

The system states facts that are not in its sources.

Detect fabricated information

Invented names, numbers, and citations pass as real.

Verify generated claims

Claims go out without a check against a source.

Ground AI outputs in reliable sources

Answers cite nothing a reader can trust or open.

Handle unusual inputs

Odd formatting, languages, or edge cases make the system fail.

Resist distribution shifts

Accuracy drops when live data no longer looks like the test set.

Maintain performance outside benchmarks

Scores on a benchmark do not hold on the real task.

Handle ambiguous inputs

The system guesses when the request could mean more than one thing.

Detect when AI does not know

The system answers with the same confidence when it has no basis.

Calibrate confidence

Stated confidence does not match how often the answer is right.

Communicate uncertainty

Users never see that the answer might be wrong.

Trigger appropriate human review

Uncertain or high-stakes cases never reach a person.

Detect degraded performance

Quality falls in production and no alert fires.

Identify abnormal behaviour

Outputs or actions drift from the pattern the system was cleared for.

Detect silent failures

Wrong answers look normal, so nobody notices the miss.

Establish meaningful failure thresholds

There is no agreed point at which a miss counts as a failure.

Reduce contradictory outputs

The same question gets incompatible answers.

Maintain behaviour across contexts

The system changes its answer when the setting changes and the facts do not.

Ensure repeatable decisions

Rerunning the same case produces a different decision.

Detect unexplained behavioural changes

Behaviour shifts after a deploy or prompt change with no recorded cause.