I'm a software engineer at Posit, where I work on Posit Package Manager — an enterprise product for managing R and Python packages inside large organizations. I've been writing code professionally for a long time, mostly in Go, and the intersection of speech and AI tooling is where I find myself spending more and more of my curiosity these days.
Parleq started, like a lot of these projects, as a workaround.
I'd been a happy user of an AI dictation app on macOS — the kind that listens, cleans up your speech with an LLM, and pastes the result wherever your cursor is. It worked well. Then I tried to use it at work, and ran into a wall: the app routes audio and transcripts through its own SaaS backend, and my employer's policies — like a lot of companies' — require that the infrastructure and models I send work through are on a pre-approved list. A new vendor that hadn't been through that review wasn't something I could keep using day-to-day, no matter how nice the product was.
So I started thinking about what an open-source, local-first version would look like — and what it would take to make it actually pleasant to use, not just defensible on a compliance review.
Two things I really wanted.
Low latency.
Dictation falls apart the moment you have to wait for it. If there's a noticeable pause between releasing the hotkey and seeing your text, you stop trusting the tool, and the rhythm of writing-by-speaking breaks. Parleq runs the speech model entirely on the Apple Neural Engine, locally, with no network round-trip. The cleanup pass is the only step that touches a remote service, and even that is optional — you can skip it and just paste the raw transcript if you want.
Post-processing that listens to me.
Raw speech-to-text output is rarely what you want pasted into Slack. You want punctuation, capitalization, the right spelling of names you keep saying, the "ums" and "uhs" cleaned up, half-sentences finished. An AI cleanup pass is great at this — but the moment it misreads your intent, you need a way to tell it what to do, not click a fix-it button. So Parleq lets you re-trigger the hotkey while the cleaned text is still on screen and just say what to change. "Make it more professional." "Shorter." "Add a question mark and end with thanks." The overlay shows you the result before it pastes, so you can keep refining until it lands.
That overlay-and-iterate loop is the part of Parleq I'm proudest of.
It treats the cleaned text as a draft you talk to, not a final output you accept or reject.
Open-source, bring your own LLM.
Parleq doesn't have a backend. There's no Parleq cloud, no Parleq account, no Parleq subscription, no Parleq SaaS to audit. Speech recognition is on-device. Cleanup runs on-device too, or against an LLM provider you configure — Gemini, OpenAI, Vertex AI, AWS Bedrock, or Azure OpenAI — using your own credentials, your organization's existing cloud accounts, and (where it matters) the same identity and access controls you've already vetted for everything else.
I built it that way partly because it sidesteps a lot of complexity I didn't want to take on, and partly because it's the most honest answer for compliance-sensitive work I could come up with. If your security team has already approved your laptop hitting Bedrock or Vertex directly, then Parleq fits inside that approval — no separate vendor review, no separate data-flow diagram, no new place where transcripts pile up.
If you're an IT admin rather than the end user, that same posture extends to fleet deployment. Parleq supports macOS Managed Configuration — you can push a .mobileconfig profile via your existing MDM that pins the LLM provider, restricts the model list, disables optional features, and prevents users from changing any of it. No Parleq admin portal needed. The Admin Guide has the full deployment walkthrough.
The whole thing is on GitHub under Apache-2.0. Audio never leaves your Mac. Logs carry length-only diagnostics, never transcript content. API keys live in the macOS Keychain.
Parleq stands on the shoulders of open-source software — Apple's Swift libraries, Sparkle for auto-updates, Soto for AWS, FluidAudio for on-device ASR, and others. The full list of dependencies and their licenses lives at THIRD_PARTY_LICENSES.md in the repo (also linked from the Parleq window's About section).
— Jon
Thanks.
A particular thanks to Jake Struzik, whose Speek showed me what a clean, local-first dictation tool on macOS could feel like. Parleq's design owes a debt to it.
Parleq is built by Keavi LLC, a Virginia limited liability company — and it's Keavi's first public, open-source project. Contact: hello@parleq.app · keavi.app