Announcing our Series A Funding

Announcing our Series A Funding

Friendly Voices

For Indian customer facing work the deciding factor is not tone but language. These voices switch between Hindi and English mid sentence, which reads as considerably more natural than either alone.

VOICES FOR THIS USE CASE

Fennafenna
FemaleYoungDutch
Best for DutchUse voice
Venlavenla
FemaleYoungFinnish
Best for FinnishUse voice
Ariaaria
FemaleYoungJapanese
Best for JapaneseUse voice
Asherasher
MaleYoungAmerican
Best for EnglishUse voice
Nicolasnicolas
MaleYoungFrench
Best for FrenchUse voice
Indranilindranil
MaleYoungIndian
Best for HindiUse voice

No tone field to filter on

The catalog has no tone field, so friendliness cannot be filtered and these voices were chosen by listening. Stating that plainly is more useful than presenting an inferred shortlist as a filtered one. Audition all six before deciding, since nothing documents the differences between them.

The one control available

Speed, from 0.5 to 2.0, is the only delivery control. There is no tone or emotion parameter, so a voice cannot be softened after generation. A setting slightly below 1.0 reads as less rushed, which in support contexts often reads as friendlier.

Code-switching sounds friendlier in India

For Indian customer facing work, the more consequential factor is not tone but language. Voices in the Indic family switch between Hindi and English mid sentence, which is how people actually speak and tends to read as considerably more natural than either language used strictly alone.

Where it matters most

Customer support, onboarding and confirmations are the usual contexts. Nine voices carry the Voice Agent tag and 195 the broader conversational tag, which is the pool worth starting from. As always the test is your own scripts rather than sample text.

SPECIFICATION

Sample rate

44.1 kHz native, resampled to 8 kHz for telephony

Latency

~200 ms to first byte (p50, warm region)

Output formats

ulaw, alaw, PCM 16-bit, WAV, MP3

Streaming transports

WebSocket, HTTP chunked transfer

Speed range

0.5× – 2.0×, set per request

Explore Voice Similar to

Friendly Voices

Warm Voices

There is no tone field in the catalog, so these were chosen by listening rather than filtered. Warmth is a property of the voice, not a setting, which makes casting the decision that counts.

Cheerful Voices

Only six voices in the catalog show cheerful or positive markers, the thinnest tone signal available. Speed slightly above 1.0 lifts almost any voice, which is the more dependable route.

Conversational Voices

One hundred and ninety five voices carry the conversational tag, 84 percent of the catalog. Streaming is what makes them feel live: audio begins playing before the sentence has finished rendering.

Young Voices

One hundred and forty six voices are tagged young, 63 percent of the catalog, across every accent and all twelve recommended languages. Unlike tone, age is a real tagged field rather than an inference.

AI Voices for Customer Support Automation

Support calls reach people who are already inconvenienced, often on a poor line. These voices favour clarity over character, hold steady across renders, and move between Hindi and English the way callers actually do.

AI Voices for Voice Chatbots

Web chat is unusual because the visitor reads and listens at once. These voices suit an unhurried delivery, begin playing in about 200 milliseconds over WebSocket, and cover twelve languages from one integration.

Female Voices

One hundred and one female voices, spanning six accent groups and all twelve recommended languages. At that scale accent and language usually constrain casting more than gender does.

Male Voices

One hundred and twenty male voices across every accent group and all twelve recommended languages. Most carry an Indic recommendation, which is unusual in catalogs that treat Indian languages as an afterthought.

Professional Voices

Corporate narration needs consistent terminology more than it needs a particular tone. Pronunciation dictionaries fix brand and product names once, so every module in a library matches the first one.

Serious Voices

For compliance and legal reads the goal is that exact wording lands, not that the voice sounds grave. A speed near 0.9 improves intelligibility more than any casting choice.

Warm Voices

There is no tone field in the catalog, so these were chosen by listening rather than filtered. Warmth is a property of the voice, not a setting, which makes casting the decision that counts.

Cheerful Voices

Only six voices in the catalog show cheerful or positive markers, the thinnest tone signal available. Speed slightly above 1.0 lifts almost any voice, which is the more dependable route.

Conversational Voices

One hundred and ninety five voices carry the conversational tag, 84 percent of the catalog. Streaming is what makes them feel live: audio begins playing before the sentence has finished rendering.

Young Voices

One hundred and forty six voices are tagged young, 63 percent of the catalog, across every accent and all twelve recommended languages. Unlike tone, age is a real tagged field rather than an inference.

AI Voices for Customer Support Automation

Support calls reach people who are already inconvenienced, often on a poor line. These voices favour clarity over character, hold steady across renders, and move between Hindi and English the way callers actually do.

AI Voices for Voice Chatbots

Web chat is unusual because the visitor reads and listens at once. These voices suit an unhurried delivery, begin playing in about 200 milliseconds over WebSocket, and cover twelve languages from one integration.

Female Voices

One hundred and one female voices, spanning six accent groups and all twelve recommended languages. At that scale accent and language usually constrain casting more than gender does.

Male Voices

One hundred and twenty male voices across every accent group and all twelve recommended languages. Most carry an Indic recommendation, which is unusual in catalogs that treat Indian languages as an afterthought.

FAQs

There is no tone field, so these were picked by ear rather than filtered. Aditi and Aarushi in Hindi, Bellatrix and Alec in English. For customer facing work, the Hindi voices can switch to English mid-sentence, which tends to read as more natural to Indian callers than either language used alone.