Phone-based onboarding sounded robotic across three languages. Natural AI voices turned calls into conversations frontline workers trust.
A workforce engagement platform that hires, onboards, and manages India's frontline workers primarily through phone-based voice interactions across Hindi, English, and Tamil.
Operating entirely over phone calls, the platform needed voice interactions that feel credible and natural across three languages. Robotic-sounding...
“At Hunar, our mission is to build meaningful workforce engagement for India's frontline ecosystem, and ElevenLabs has helped us strengthen a key part of that experience through more natural voice interactions.”
AI platform for frontline workforce hiring, onboarding, and management.
AI voice synthesis platform for text-to-speech, dubbing, and voice cloning.
Hunar AI's Voice agents is part of this use case:
Related implementations across industries and use cases
Voice integration demanded 400 lines of code. A pre-built framework cuts that to 40, enabling rapid agent deployment.
Without emotional warmth, users felt self-conscious during personal AI chats. Expressive text-to-speech made conversations feel human.
Legacy models caused 41 escalated cases. A 90-day switch to cloud AI restored accuracy and enabled real-time analytics.
Callers instantly hang up on robotic voices. Emotive AI voices with regional accents now hold natural, real-time conversations.
Order inquiries piled up. A bilingual voice bot now resolves delays and refunds in Hindi and English with human-like warmth.
Scattered data and basic coding tools bottlenecked engineers. A 9-agent AI workflow shifts them from writing code to directing AI teams.
Sequential AI testing bottlenecked development. Engineers built a concurrent, code-first pipeline to evaluate agent responses in seconds.
A 200% yearly data expansion bottlenecked global operations. Now, AI accelerates coding, drafts recipe cards, and resolves inquiries.
Moderation couldn't keep pace with 600M users. AI agents now filter toxicity while models recognize 2.5B objects to refine search.
Phone-based onboarding sounded robotic across three languages. Natural AI voices turned calls into conversations frontline workers trust.
A workforce engagement platform that hires, onboards, and manages India's frontline workers primarily through phone-based voice interactions across Hindi, English, and Tamil.
Operating entirely over phone calls, the platform needed voice interactions that feel credible and natural across three languages. Robotic-sounding...
“At Hunar, our mission is to build meaningful workforce engagement for India's frontline ecosystem, and ElevenLabs has helped us strengthen a key part of that experience through more natural voice interactions.”
AI platform for frontline workforce hiring, onboarding, and management.
AI voice synthesis platform for text-to-speech, dubbing, and voice cloning.
Hunar AI's Voice agents is part of this use case:
Related implementations across industries and use cases
Voice integration demanded 400 lines of code. A pre-built framework cuts that to 40, enabling rapid agent deployment.
Without emotional warmth, users felt self-conscious during personal AI chats. Expressive text-to-speech made conversations feel human.
Legacy models caused 41 escalated cases. A 90-day switch to cloud AI restored accuracy and enabled real-time analytics.
Callers instantly hang up on robotic voices. Emotive AI voices with regional accents now hold natural, real-time conversations.
Order inquiries piled up. A bilingual voice bot now resolves delays and refunds in Hindi and English with human-like warmth.
Scattered data and basic coding tools bottlenecked engineers. A 9-agent AI workflow shifts them from writing code to directing AI teams.
Sequential AI testing bottlenecked development. Engineers built a concurrent, code-first pipeline to evaluate agent responses in seconds.
A 200% yearly data expansion bottlenecked global operations. Now, AI accelerates coding, drafts recipe cards, and resolves inquiries.
Moderation couldn't keep pace with 600M users. AI agents now filter toxicity while models recognize 2.5B objects to refine search.