You Can't Improve What You Can't Measure
Standard LLM benchmarks were built for English. They don't capture what matters for Indian languages—native fluency, code-mixing, cultural context, and regional accuracy. Without proper evaluation, you're flying blind.
Translation is Not Evaluation
Translating English benchmarks to Hindi doesn't test Hindi capability. It tests translation quality. Native speakers don't think in translated sentences.
Code-Mixing Ignored
Real Indians speak Hinglish, Tanglish, Benglish. Standard benchmarks pretend this doesn't exist. Your model fails in the real world.
Hallucination Detection Fails
Factual accuracy checks need Indian knowledge bases. Western benchmarks don't know that Diwali dates change yearly or that Indian states have different official languages.
Cultural Context Missing
Sentiment analysis trained on Western data misreads Indian communication patterns. Respectful language varies by region, generation, and context.
You can't improve what you can't measure. Indic Eval measures what matters.
The Indic Eval Solution
A comprehensive evaluation framework built from the ground up for Indian languages—with native test cases, code-mixed scenarios, and cultural context awareness.
Native-first test cases
Test cases created by native speakers in native thought patterns. Not translations.
Code-mixing as standard
Hinglish, Tanglish, Benglish, and other code-mixed variants tested explicitly.
Indian knowledge validation
Factual accuracy against Indian context—geography, history, culture, current events.
Cultural sensitivity scoring
Evaluate appropriateness across regions, communities, and communication contexts.