Language support

Teaching in Indic languages should sound like you, not like a textbook.

Spark turns recordings into structured courses. When the teaching is in Hindi, Tamil, or other Indic languages, that pipeline has to clear a higher bar than English-only content. We care about accuracy, how natural the study text feels, and whether it still sounds like the same instructor.

Three quality gates

Before we are comfortable shipping support for a language, we work through three gates: transcription quality (how faithfully speech becomes text), course content quality (notes and quizzes that read well in that language), and instructor voice (output that fits how you actually teach). Each gate turned out harder than we expected for Indic languages, for structural reasons in data and models, not because teachers are doing anything wrong.

Transcription: error rates and provider quality vary a lot by language. We route and evaluate per language instead of assuming one global ASR is best everywhere.
Naturalness: large models often default to stiff, formal register in Indic languages. Getting classroom-appropriate tone is a product and research problem, not a one-line prompt fix.
Instructor voice: even good generic prose can miss the way you explain to your students. We treat that as a first-class problem alongside correctness.

Read the full whitepaper

We published a long-form paper on the Indic language landscape, benchmarks, cost dynamics, and how we think about architecture. It is written for fellow builders and for anyone who wants to understand why multilingual AI is still hard in practice.

Spoken by Billions, heard by few

For the rest of how Spark handles accuracy and safety across languages, see AI accuracy & safety.