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: these all turn into text an AI agent or a search index can use, and text turns 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, a voice message, a call recording, a video’s narration, into text a system can search, index or feed to an AI agent. Text-to-speech does the reverse, turning generated or written text into spoken audio, for a voice agent, an accessibility feature, or narrated content. Both directions matter for different reasons: transcription unlocks voice content for search and automation, synthesis lets a product respond 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 contain information you currently cannot search, act on, or feed to an AI agent, a support team fielding voice messages on WhatsApp with no way to scan them for urgent ones, or a video content pipeline that needs captions and a searchable transcript. You need synthesis once a product needs to speak, a voice agent on a phone line, narrated video content, an accessibility feature reading content aloud.
You do not need a custom integration if your current platform, a call center tool, a video editor, already includes transcription or voice synthesis that is good enough for the use case, building a custom pipeline to replace a feature that already works is wasted budget. The signal you have outgrown the built-in option is a specific requirement it does not meet: a language it does not support well, a latency requirement it cannot hit, or a cost at your volume that is no longer reasonable.
How we build it
For transcription we default to OpenAI’s Whisper, including the open-source version, self-hosted, when data residency or cost at volume makes that the better fit, and Google’s Speech-to-Text where its language coverage or specific accuracy on a target language is better. We test against real audio from your actual use case before committing to a provider, a clean studio demo tells you very little about how a model performs on a WhatsApp voice message recorded in a noisy environment. For synthesis, voice choice matters as much as the underlying technology, we pick a voice that matches your brand rather than defaulting to whatever sounds best in a provider’s demo. Latency is tuned to the use case: a voice agent on a live call needs near-real-time response, while transcribing a backlog of video content for a content pipeline 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, where getting transcription and narration both working reliably across real, imperfect audio was the actual engineering challenge, not the API call itself.
What to watch
The gap between a provider’s advertised accuracy and real performance on your actual audio is the single biggest risk, background noise, accents, overlapping speech and audio compression on a messenger app all degrade transcription quality in ways a clean benchmark does not reveal. We test against real samples from your use case specifically to catch this before launch, not after users complain about bad transcripts. For anything feeding an automated decision, we add a confirmation step rather than trusting a transcription at face value, the cost of a misheard word mattering is very different for a search index than for a financial instruction. Cost of ownership scales with volume for most cloud providers; self-hosting Whisper trades a cloud bill for infrastructure and maintenance, a tradeoff worth making only past a certain volume, which we calculate with you rather than assume.
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), Google's Speech-to-Text, and ElevenLabs or a provider's native voice for text-to-speech. We choose per use case, not a single default across every project.
Does this work for languages other than English?
Yes, and we test against your actual target languages before launch rather than assuming a provider's advertised multilingual support performs equally well across all of them, it usually does not.
What happens if the transcription is wrong?
Depends on the stakes: for a search or indexing use case, occasional errors are acceptable and get corrected over time. For anything feeding a decision, a support agent acting on a transcribed request, we add a confirmation step rather than trusting a transcription blindly.