Archive · 15 Dec 2025
Humans Essential in Teaching AI Indian Languages
AI systems in India need help from humans to understand and speak Indian languages correctly.
Companies like Superbot, Devnagri AI, and Gnani.ai use experts in regional languages to train their AI models.
These experts help the AI understand different tones, dialects, and cultural nuances.
For example, a phrase in Hindi can have different meanings based on how it's said.
Humans also correct mistakes and guide the AI to learn from real-life conversations.
This makes the AI more accurate and reliable.
Without human help, AI might make big mistakes that could cause problems.
So, humans are very important in teaching AI to speak and understand Indian languages properly.
AI startups in India rely on human experts to train AI models in regional languages.
Superbot uses a team of 14 regional language experts to clean and tag datasets for ASR and TTS models.
Devnagri AI employs vernacular language experts to ensure accurate translations into Indian languages.
Gnani.ai uses human-labelled instances to train its AI to detect sentiment and intent in different tones.
Human input drives over 50% of the model's performance, ensuring accuracy and cultural appropriateness.
- Who
- AI startups like Superbot, Devnagri AI, and Gnani.ai
- What
- Rely on human experts to teach AI how to understand and generate Indian languages
- When
- Ongoing, with specific examples from 2025
- Where
- India, focusing on regional languages and dialects
- Why
- To ensure accuracy, cultural appropriateness, and reliability of AI systems
- Superbot
- AI-powered voice agent startup
- Automatic Speech Recognition (ASR)
- Model used for speech recognition
- Text to Speech (TTS)
- Model used for converting text to speech
- Devnagri AI
- Startup translating content into Indian vernacular languages
- Gnani.ai
- AI startup focusing on sentiment and intent detection
- Human Input
- Over 50% of model performance driven by human input
- Crowdsourcing Program
- Large-scale program to eliminate bias and build linguistic coverage