Evaluate Indian-language AI in the real service journey

Choose the languages and tasks deliberately

Define the language combinations, dialect context and type of information the service handles. A model may perform differently on a short FAQ, noisy speech and a technical complaint. Measure each selected task rather than claiming universal multilingual capability.

Use representative conversations

Include code-switching, names, addresses, numbers and incomplete speech. Check whether important details survive transcription and translation. A fluent response that changes an account number or a service commitment creates a different problem.

Connect the human handoff

The receiving agent should see the source, translated summary and uncertainty. Give customers a clear route to a person when the system cannot understand or complete the request. Evaluate the handoff as part of the experience.

Build a maintained language process

Approved terminology, relevant examples and reviewed knowledge matter as much as a language setting. Resources such as BHASHINI can be evaluated within the permitted integration scope. Product support should state the language-task combinations that have actually been assessed.

Common questions