Evaluate more than transcription
A service conversation may include regional vocabulary, code-switching, names, numbers, noisy environments and incomplete requests. Test recognition, intent understanding, response quality and whether the system asks a useful follow-up. Measure performance by task and user group, not just an average score.
Design for uncertainty and hand-off
When the system is unsure, it should say so in a comprehensible way and provide a path to a person or another channel. Establish whether the assistant may collect personal information, what it should avoid and how a human receives enough context to continue without making the user repeat everything.
Use language technology as a component, not proof
IndiaAI’s AIKosh and BHASHINI ecosystem includes language and speech resources. Availability of a model or dataset does not establish accuracy for a particular Tamil Nadu service, dialect or domain. Check licensing, coverage, privacy and task-specific performance before choosing components.
Include users in evaluation
Test with people who reflect the intended service audience and environments. Review misunderstandings, accessibility, response latency and the quality of escalation. For high-impact services, do not rely on a single automated language metric or treat translation as legal or policy validation.