Announcing our Series A Funding

Announcing our Series A Funding

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.

VOICES FOR THIS USE CASE

Catalinacatalina
FemaleYoungLatin-American
Best for SpanishUse voice
Claudiaclaudia
FemaleYoungCastilian-Standard
Best for SpanishUse voice
Andreiandrei
MaleYoungRussian
Best for RussianUse voice
Jakubjakub
MaleYoungPolish
Best for PolishUse voice
Arghyaarghya
MaleYoungIndian
Best for HindiUse voice
Muruganmurugan
MaleYoungIndian
Best for HindiUse voice

Drawn from two working pools

There is no professional tag, so this set comes from the educational and Voice Agent groups, which between them cover the contexts where a composed register matters. Twenty two voices carry one of those tags. Selection within them rests on listening rather than on any documented attribute.

Pace over character

Speed, adjustable from 0.5 to 2.0, is the only delivery control. For corporate narration a setting slightly below 1.0 reads as more measured. There is no tone parameter, so a voice cannot be made to sound more formal after the fact.

Terminology consistency

Corporate material repeats brand names, product names and acronyms constantly, and inconsistency across a library is more noticeable than any individual mispronunciation. Pronunciation dictionaries fix each term once so every generation matches, which matters more for recurring material than for one off scripts.

Consistency across a long script

Requests cap at 250 characters, so a quarterly presentation or training script is assembled from many calls. Keeping voice, speed and sample rate fixed across the whole run removes most variation, but generating a full section and listening across the joins is still worth doing before committing.

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

Professional Voices

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.

Authoritative Voices

Slowing down reads as more authoritative than speeding up. A setting near 0.9 does more than any voice choice, and short declarative sentences carry more weight than qualified ones.

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.

Mature Voices

Eighty six voices are tagged mature, and twenty also carry the narrative tag. That overlap is the usual starting point for audiobooks and documentary work, where consistency shows over hours rather than seconds.

AI Voices for E-Learning

Course audio has to sound the same in module ten as in module one. These voices hold consistent across sessions, handle technical vocabulary through pronunciation dictionaries, and cover nine Indic languages alongside English.

AI Voices for IVR & Telephony

IVR voices need to survive a compressed phone line, not just sound good in a browser. These output ulaw and alaw directly, generate in roughly 200 milliseconds, and handle Hindi and English in the same prompt.

Deep Voices

Worth saying plainly: nothing in the catalog records pitch, so these six were chosen by ear rather than filtered. There is no pitch control either, so depth is a casting decision.

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.

Energetic Voices

Speed is the reliable lever here, not casting. Settings between 1.2 and 1.4 lift almost any voice in the catalog, though longer sentences start to blur past roughly 1.5.

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.

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.

Authoritative Voices

Slowing down reads as more authoritative than speeding up. A setting near 0.9 does more than any voice choice, and short declarative sentences carry more weight than qualified ones.

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.

Mature Voices

Eighty six voices are tagged mature, and twenty also carry the narrative tag. That overlap is the usual starting point for audiobooks and documentary work, where consistency shows over hours rather than seconds.

AI Voices for E-Learning

Course audio has to sound the same in module ten as in module one. These voices hold consistent across sessions, handle technical vocabulary through pronunciation dictionaries, and cover nine Indic languages alongside English.

AI Voices for IVR & Telephony

IVR voices need to survive a compressed phone line, not just sound good in a browser. These output ulaw and alaw directly, generate in roughly 200 milliseconds, and handle Hindi and English in the same prompt.

Deep Voices

Worth saying plainly: nothing in the catalog records pitch, so these six were chosen by ear rather than filtered. There is no pitch control either, so depth is a casting decision.

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.

FAQs

Drawn from the educational and voice agent pools, since there is no corporate tag. Alec and Erica read as composed in English, Aarushi and Aditi in Hindi. For recurring material, pronunciation dictionaries are the feature that matters most, since they fix brand and product names once rather than per script.