Voice in, voice out
speech recognition and synthesis wired into your product
Voice messages on Telegram and WhatsApp, support calls, video narration, a voicemail nobody transcribes. All of it can turn into text an AI agent or a search index can use. Text can turn back into speech when a product needs to talk. We wire both directions in, matched to your language and latency needs.
What it is
Speech-to-text converts spoken audio into text. A voice message, a call recording, a video’s narration, turned into something a system can search, index or hand to an AI agent. Text-to-speech does the reverse: generated or written text becomes spoken audio, for a voice agent, an accessibility feature, or narrated content. Transcription opens up voice content for search and automation. Synthesis lets a product answer back in a voice instead of only in text.
When you need it, and when you do not
You need transcription once voice messages or calls hold information you cannot search, act on, or feed to an AI agent today. A support team fielding WhatsApp voice messages with no way to scan for the urgent ones is one example. A video pipeline that needs captions and a searchable transcript is another. You need synthesis once a product has to speak: a voice agent on a phone line, narrated video content, or an accessibility feature reading content aloud.
You do not need a custom integration if your current tool already covers it well. A call center platform or a video editor often ships transcription or voice synthesis good enough for the job, and replacing something that already works is wasted budget. The real signal is a specific gap the built-in option leaves open. A language it handles poorly, a latency target it cannot hit, or a cost that stops making sense at your volume.
How we build it
For transcription we default to OpenAI’s Whisper. That includes the open-source version, self-hosted, when data residency or cost at volume makes that the better fit. We also use Google’s Speech-to-Text where its language coverage or accuracy on a target language is better. We test against real audio from your use case before picking a provider. A clean studio demo says little about a WhatsApp voice message recorded on a noisy street.
For synthesis, the voice you choose matters as much as the engine behind it. We pick a voice that matches your brand instead of whatever sounds best in a provider’s demo. Latency gets tuned to the use case. A voice agent on a live call needs a near-instant response. Transcribing a backlog of video content can run in batch overnight, at a fraction of the cost. We built this kind of pipeline into a Telegram reels editor and a broader video content pipeline. In both, getting transcription and narration to hold up on real, imperfect audio was the actual engineering work, not the API call itself.
What to watch
The gap between a provider’s advertised accuracy and its real performance on your audio is the biggest risk here. Background noise, accents, overlapping speech and a messenger app’s audio compression all chip away at transcription quality in ways a clean benchmark never shows. We test against real samples from your own use case to catch that before launch, not after users start complaining about bad transcripts.
For anything feeding an automated decision, we add a confirmation step instead of trusting a transcription at face value. A misheard word costs very little in a search index and a lot in a financial instruction. Cost of ownership also scales with volume for most cloud providers. Self-hosting Whisper trades the cloud bill for infrastructure and upkeep. That trade is only worth making past a certain volume, and we work that number out with you.
Price and timeline
| Scope | Price | Timeline |
|---|---|---|
| Single direction, batch processing | from $1,200 | 1 to 2 weeks |
| Two-way, real-time latency | from $3,000 | 2 to 3 weeks |
Related
Built as part of AI agents and custom development. Often paired with an AI agent runtime with tools and approvals when voice feeds a conversational agent. See it running in a Telegram reels editor and an AI video content pipeline. Tell us what voice content you need to process: get in touch.
FAQ
How much does speech-to-text or text-to-speech integration cost?
A single-direction integration, transcribing voice messages into an existing chatbot for instance, starts at $1,200. A fuller two-way voice pipeline with real-time latency for a voice agent runs $2,500 to $5,000.
How long does it take?
1 to 3 weeks, depending on whether real-time latency is required and how many languages or accents need testing. Batch transcription for existing content is usually the faster build.
Which providers do you use?
OpenAI's Whisper, including the open-source model self-hosted when cost or data residency matters. Also Google's Speech-to-Text, and ElevenLabs or a provider's native voice for text-to-speech. We choose per use case, not one default across every project.
Does this work for languages other than English?
Yes. We test against your actual target languages before launch. A provider's advertised multilingual support rarely performs equally well across all of them, so we check rather than assume.
What happens if the transcription is wrong?
It depends on the stakes. For a search or indexing use case, occasional errors are fine and get corrected over time. For anything feeding a decision, like a support agent acting on a transcribed request, we add a confirmation step instead of trusting the transcript blindly.