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Speak your task.
Let AI code.

Dictate coding tasks in RU, EN, or translate between them mid-sentence. Brew Voice auto-inserts method names and stack terms from the project context you control, then hands the prompt to Claude Code, Codex, Cursor, or any LLM. ~6 seconds for 200 seconds of speech.

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REC · 00:42
RU ⇄ EN · translate
Raw speech → Clean prompt (or translated message) for your AI agent or teammate

Dictate the task. Paste the prompt. Let the agent code.

Brew Voice keeps the task conversational: it just fixes filler, punctuation, and the casing of identifiers from your repo. Or translate mid-sentence into another language. The result is a prompt Claude Code, Codex, or Cursor will actually understand, or a clean message your teammate can read.

● Raw speech (RU) 28 sec · unedited
так слушай там в юзер-сервисе этот метод финд бай айди он возвращает нулл если юзера нет так нельзя надо чтобы кидало юзер нот фаунд эксепшн ну и в контроллере надо обработать замапь на 404 и да тесты напиши там юнит-тесты на сервис и на контроллер
▶︎ Clean prompt (RU) ready to paste
В `UserService.findById()` сейчас возвращается `null`, если юзера нет. Надо, чтобы вместо этого кидало `UserNotFoundException`. В `UserController` обработай это исключение, замапь на HTTP 404. Напиши юнит-тесты: на сервис и на контроллер.
● Raw speech (EN) 26 sec · unedited
ok so in user service that find by id method it returns null if the user isn't there that's wrong it should throw a user not found exception and in the controller we need to handle that map it to a 404 also write the tests unit tests for both the service and the controller
▶︎ Clean prompt (EN) ready to paste
In `UserService.findById()`, it currently returns `null` when the user isn't found. It should throw `UserNotFoundException` instead. In `UserController`, handle that exception, map it to HTTP 404. Write unit tests for both the service and the controller.
● Raw speech (RU) 14 sec · unedited
привет слушай там в слаке напиши пожалуйста что деплой задерживается на два часа из-за инцидента с базой и я вернусь после обеда
▶︎ Translated message (EN) ready to paste
Hey, could you please write in Slack that the deploy is delayed by two hours due to a database incident, and I'll be back after lunch.
Try it on your repo
Speed

Fixed pipeline. Predictable latency.

No cloud queue roulette. 200s of speech processed in ~6s, scaled.

Your voice input (200s of speech) 200s
Brew Voice processing 6s
Typical competitor (peak hours) 45s+ variable

Batching + conveyor-style inference. Competitors' SaaS latency varies 10–60s under load.

By role

Built for the work you're already doing.

Same engine, different defaults. Pick the workflow closest to yours.

Describe the behavior. Let the agent code it.

Dictate the task in plain words. Brew Voice keeps your Kotlin/Java/Go class names, method signatures, and exception types intact, ready to paste into Claude Code.

  • Auto-cased class & method names
  • Inline `code` formatting for identifiers
  • Stays conversational, not PR-style
Dictated task
"в юзер сервисе финд бай айди возвращает нулл надо чтоб кидал юзер нот фаунд эксепшн и тесты напиши"
Clean prompt
В `UserService.findById()` возвращается `null`. Сделай, чтобы кидало `UserNotFoundException`. Напиши юнит-тесты.

Speak the component you want to build

Describe UI behavior out loud. Brew Voice produces a clean task for your agent, with component names, prop types, and state hooks all from your repo.

  • Recognizes React / Vue / Svelte conventions
  • Matches component names already in your tree
  • Keeps prompts framework-idiomatic
Dictated task
"в юзер карде добавь лоадинг стейт пока юзер грузится покажи скелетон и задизейбл фоллоу кнопку"
Clean prompt
В `UserCard` добавь loading state. Пока `user` грузится, покажи `<Skeleton />` и задизейбл `FollowButton`.

Dictate the ops task. Paste into your agent.

Terraform resources, kubectl manifests, helm chart tweaks: describe the change in plain words, get an LLM-ready prompt with canonical names.

  • k8s / helm / terraform vocabularies
  • Namespace + resource names from your cluster
  • No mangled resource identifiers
Dictated task
"пофикси терраформ ресурс aws эс три бакет логс нужно включить версионинг и шифрование kms"
Clean prompt
Обнови `aws_s3_bucket.logs` в Terraform: включи versioning и server-side encryption через `kms_master_key_id`.

Say the refactor. Skip the typing.

Extract method, rename, pull members up, change signatures. Brew Voice turns the description into a crisp prompt the agent can act on without guessing.

  • Correct identifier casing, always
  • Keeps scope explicit (file, class, package)
  • No invented method names
Dictated task
"в пеймент процессоре метод хэндл транзакшн слишком большой вынеси валидацию в отдельный приватный метод"
Clean prompt
В `PaymentProcessor.handleTransaction()` вынеси блок валидации в отдельный `private` метод `validateTransaction()`. Не меняй публичный контракт.
Project Context

Your codebase knows what you meant to say.

Brew Voice indexes your repo locally. When you dictate a task, the names, terms, and stack you actually use win every tie, so the prompt your agent sees is unambiguous.

Controlled context

You manage the vocabulary. Repo indexed? Only what you whitelist gets injected.

whitelist → active context

Method & class names

Dictate "юз кейс сервис" → get UseCaseService. Exact as in your repo.

"юз кейс сервис" → UseCaseService

Stack-aware

kubectl apply, Dockerfile, ConfigMap inserted exactly as written in your config files.

kubectl apply -f deploy.yaml
Why Brew Voice

Fast. Multilingual. Under your control.

A voice pipeline built for vibe-coding: predictable speed, honest multi-language, and a context you actually manage.

Predictable speed

Fixed pipeline, batching, conveyor inference. 200s voice → ~6s. No cloud queue roulette.

Translation built-in

Dictate in RU, get EN. Or vice versa. Mid-sentence. Slack your coworker in their language without stopping to type.

Good Russian + EN

Not a "multi-lang" afterthought. Both languages first-class, mix freely in one session.

Managed project context

You decide which repo, which vocabulary, which stack terms get inserted. Explicit control, not a black-box autocomplete.

Cheap SaaS pricing

GPU-efficient pipeline. We pass the savings along. Details at launch.

Enterprise self-hosted

Need on-prem? Deploy on your infra. Contact us for Enterprise pricing.

Curious? Ask your AI assistant.

It's heard about us. Probably.

Ask Claude claude.ai Ask ChatGPT chat.openai.com Ask Perplexity perplexity.ai

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