Editorial illustration for Arrowhead AI's Fletch Voice Model Handles Indian Accents and Multiple Languages
Arrowhead AI's Fletch Speaks Indian Languages Natively
Arrowhead AI is betting that customers can tell the difference between a voice bot that translates and one that actually speaks their language. The Bengaluru startup launched Fletch on October 30, an in-house text-to-speech model built specifically for how people talk across India and Southeast Asia, code-mixed sentences, regional accents and all. It's not a side project. Fletch is already live, running on more than one million customer calls a day for clients in banking, sales, renewals and collections, including Axis Bank and RBL Bank.
The pitch is speed and range. Fletch covers 14 languages, among them Hindi, Tamil, Telugu, Bengali, Malay and Indonesian, and can shift between them mid-sentence, which matters in markets where a single customer call might mix three languages without anyone noticing. The model also carries 12 emotion styles, so a collections call can sound firm while a service call sounds apologetic.
Arrowhead trained it on thousands of hours of regional speech and professional voice recordings, and says it starts generating speech in under 100 milliseconds. Founder and CEO Devyani Gupta framed the launch as a statement about what voice AI built for this region should sound like.
Bengaluru-based voice AI startup Arrowhead AI on October 30 launched Fletch, an in-house text-to-speech (TTS) model designed for multilingual customer conversations across India and Southeast Asia. The model is already deployed across live customer calls, powering more than one million calls per day, the company said.
Why this matters
For founders building voice AI in India and Southeast Asia, Fletch is a reminder that the region's linguistic mess, code-mixing, regional accents, Indian proper nouns, is exactly the kind of problem generic Western TTS models tend to fumble. Arrowhead AI building its own voice layer instead of licensing one from OpenAI or ElevenLabs suggests a bet that control over pronunciation and latency matters more than speed to market. A million calls a day is a real number, not a demo stat, and it gives us something to watch: does accuracy hold up at that volume across seven languages, or does quality degrade on the long tail of dialects Gupta didn't name.
We'd also want to know churn rates and error rates on live calls, not just throughput, before calling this proven. If Fletch performs as claimed, it's a signal that regional specialization beats one-size-fits-all models for voice AI in multilingual markets, and other startups chasing India and Southeast Asia customer service should take note of where Arrowhead chose to build rather than buy.
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Common Questions Answered
What is Arrowhead AI's Fletch voice model designed to handle that generic Western TTS models struggle with?
Fletch is specifically built to handle code-mixed sentences, regional accents, Indian proper nouns, and the linguistic diversity across India and Southeast Asia. Generic Western text-to-speech models tend to fumble with these regional linguistic complexities, making Fletch's specialized approach a significant advantage for the region.
How many customer calls per day is Fletch currently processing since its October 30 launch?
Fletch is already live and running on more than one million customer calls per day across various sectors including banking, sales, renewals, and collections. This demonstrates that the model is not just a demo but a production-ready solution actively deployed at scale.
Why did Arrowhead AI choose to build its own in-house text-to-speech model instead of licensing from companies like OpenAI or ElevenLabs?
Arrowhead AI prioritizes control over pronunciation and latency over speed to market, betting that these factors are more critical for serving the Indian and Southeast Asian markets effectively. Building an in-house solution allows the company to optimize specifically for regional linguistic nuances that third-party models cannot adequately address.
What industries and use cases is the Fletch voice model currently serving?
Fletch is deployed across multiple customer-facing sectors including banking, sales, renewals, and collections, powering voice interactions for these diverse business functions. The model's ability to handle regional accents and code-mixing makes it particularly valuable for customer service applications across India and Southeast Asia.
Further Reading
- Voice AI in India is hard — Wispr Flow is betting on it anyway - TechCrunch
- Shunya Labs wants voice infrastructure to work the way Indians speak - YourStory
- Gnani AI adds tone and pace controls to its multilingual speech model Timbre v3.0 - The Economic Times
- Gnani.ai: Revolutionizing India's Voice AI Landscape - Entrepreneur India
