Reliability for
multilingual AI.
See where meaning degrades across speech, translation, and LLM workflows.
Operationally healthy does not always mean semantically correct.
Traditional observability tells you whether the request succeeded.
We tell you whether the meaning survived.
Every stage can return 200 OK while the meaning quietly changes.
See what changed
at every stage.
Run a code-mixed workflow and see the exact stage where it failed.
Sample workflow input
Mera card block mat karna, bas ₹5,000 ki limit set kar do.
Hinglish · Hindi spoken with English, transcribed in Latin script — “Do not block my card; just set its limit to ₹5,000.”
Built to find the failures
dashboards miss.
Opportune evaluates multilingual workflows across speech, translation, LLM, and generation layers, and catches semantic drift before it reaches production.
Semantic fidelity
Did the requested meaning survive?
Entity preservation
Were names and identifiers preserved?
Number and time
Were amounts, dates, and times preserved?
Language and script
Did the response stay linguistically consistent?
Output stability
Does the same input stay reliable across repeat runs?
Regression tracking
Did a model or prompt change introduce drift?
Indian languages first. Global failure modes.
Beyond traces.
Measure whether meaning survived.
Catch the failures your dashboards call successful.