A practical test for separating useful AI capability from impressive but unsupported claims.
AI announcements can move faster than evidence. A useful assessment starts with the task, the data and the consequence of a wrong answer.
Look for the population, test conditions and comparison point behind any accuracy or productivity claim.
Reliable systems make uncertainty visible and give people a clear way to review, correct or reject an output.
Why our products separate extracted facts, user explanations and automated analysis before reaching a conclusion.
Human oversight works only when reviewers have authority, context and enough time to change the outcome.
Language, trust, regulation and everyday constraints determine whether technology becomes genuinely useful.
Resilient digital products preserve progress, explain status and recover cleanly when networks fail.
