Choose your mic
for audio you can trust.
Speak AI reads whatever a microphone hands it: the words, the pace, and who is speaking, on every call and meeting. The best microphone for AI transcription is the one that gets a clean signal in, and the AI takes it from there. We build it with you.
The wins teams ship.
Time to a live product, hours saved per file, and dollars saved. Same platform, very different applications.
Legal tech company builds a white-label deposition platform, 8 months faster.
Global research agency launches a white-label qualitative research platform.
Legal intelligence firm processes 5,100+ hours of carrier calls, 95% faster.
Healthcare consulting firm cut session processing from 8 hours to 0.3.
E-commerce manufacturer centralizes call review and cuts it by 85%.
Recruiting firm cuts candidate report time from 5 hours to 10 minutes.
Bring your setup. Leave with a clean transcript.
A working session, not a sales pitch. No obligation.
You bring your setup
Your laptop mic, headset, conference room, or podcast rig. Whatever you record meetings and calls on today.
We check your capture chain
Mic choice, room acoustics, and platform settings, mapped against the transcription accuracy your team needs.
You see it transcribed, live
Your own recording, transcribed and scored on your own criteria, with a rollout plan for the whole team.
The right mic, matched to how your team meets.
Speak AI transcribes whatever you capture with, and reads better the cleaner the input is.
Remote sales calls
A USB condenser or a good headset mic keeps discovery calls and objections legible, so scoring and coaching read the actual words, not guesses.
Contact center desks
Noise-cancelling headsets cut hold-music bleed and cross-talk, so every support call transcribes cleanly enough to QA at 100%, not a 2% sample.
Conference rooms
A ceiling or tabletop array mic beats a laptop mic across a table, so hybrid meetings transcribe every voice, not just whoever sits closest.
Depositions & intake calls
A lavalier or dedicated recorder captures depositions and client intake clearly enough for a transcript your firm can rely on in the record.
Interviews & training sessions
A shotgun or lapel mic on the interviewer keeps qualitative interviews and role-play recordings transcribable, even with a quiet or distant subject.
Field recording
A handheld recorder or phone mic close to the source outperforms a laptop across a room, so field interviews still transcribe accurately later.
A different approach to microphone choice for AI transcription.
The best microphone for AI transcription is the one that gets a clean, close signal to the software: a condenser or USB mic for a quiet room, a headset or lavalier for calls and interviews, a shotgun or handheld recorder in the field. The mic does not do the transcribing. It decides how much the AI has to work with.
Why microphone choice quietly wrecks transcripts
Most teams find out their microphone was the problem after the fact: a transcript full of guessed words, a speaker who never gets identified correctly, a call that reads like it was recorded underwater. Condenser mics pick up detail and room tone, dynamic mics reject background noise but need to be close, USB mics are the easy default for a desk, and wireless lavaliers trade a little fidelity for freedom of movement. None of that is wrong on its own. The mistake is picking one without asking what the AI on the other end needs: a clear, consistent signal with the speaker close to the capsule and the room noise low.
Reading the signal, not just the words
Speak AI treats a recording the way a careful listener would, at machine speed. Speech is transcribed in your language, with 100+ supported, and the recording itself is analyzed for the three-layer language: the words, the tone, energy, and emotion in the voice, and, where available, the visuals. A clean signal from a good microphone makes every one of those layers more reliable, and a noisier one gets routed through the engine best suited to recover it, so a rushed intake call or a mid-tier headset still comes back usable.
What to check before your next recording
- “Is the mic within 6 to 12 inches of the speaker, or is it picking up the whole room?”
- “Does the noise cancellation cut a fan or an air conditioner without eating the speaker’s voice too?”
- “Is this a portable setup, or does the mic tie the recording to one desk?”
- “Does the price reflect what one bad transcript actually costs the team in re-listening time?”
From a clean signal to a searchable record
The payoff is not a better recording. It is what happens after: transcripts your team can trust the first time, sentiment and urgency scored consistently instead of some days better than others, and dashboards you can customize and white-label that track transcription accuracy and call quality over time, so this month is measured against last month instead of a gut feeling. A global entertainment leader put its FX network meetings, mixed mics and all, through this workflow and now runs automated meeting transcription and summarization across the business.
And because the same engine scores and coaches every conversation on the same criteria, once the audio lands clean it connects straight into call scoring and coaching, and every transcript, field, and score is queryable from Claude, ChatGPT, and Cursor through the MCP server.
Engineered with you, accurate from day one.
A generic AI tool starts from zero, no matter what microphone fed it. We shape the fields, scoring, and prompts around how your team captures meetings and calls, then prime the application on your existing recordings so it reads your setup correctly from the first file. You get structured data back, not just a transcript.
- We design the context, fields, and scoring around your microphones, rooms, and call types, not a template.
- Your historical recordings and transcripts prime the knowledge base before go-live, mixed mics included.
- Structured data on every recording, queryable from Claude, ChatGPT, and Cursor through the MCP server.
Bring your applications into Claude, ChatGPT, and Cursor.
No terminal. No npm. No config. Speak AI's MCP server gives any assistant 100+ tools to search, analyze, and act on your knowledge base in about 60 seconds. It is the same layer your applications run on, wired into the hundreds of apps in your stack through an integrations layer and a full developer API.
One platform. Not one model.
A generic AI tool locks you to one model and one engine. Speak AI picks the right model, speech engine, and language for each task, file type, and team, so your applications are never locked to a single vendor.
Multi-model
Claude, ChatGPT, and Gemini. Your choice per task, or bring your own key.
Multi-engine
Transcription routed across multiple engines for your audio, accents, and terms.
100+ languages
Transcribe and translate in and out, for global and multilingual teams.
MCP, API & integrations
100+ MCP tools and an integrations layer that connects to hundreds of apps you already run.
Teams build on Speak AI.
Real feedback from teams using Speak AI for research, transcription, meetings, and client work.
Questions we get
Your first scorecard runs on a real recording during the consult. Team rollout takes days, not months, because we build it with you and prime it on your existing recordings.
Pooled usage, not per-seat, with no volume minimums. Pilots are credited in full. We scope pricing for your exact workflow on the call.
Speak AI handles 100+ languages, including conversations that switch language mid-sentence, and can translate in and out.
Yes. White-label deployments run on your own domain with your logo, including client platforms agencies resell, plus branded iOS and Android apps.
It is a placement guideline: keep any second microphone at least three times farther from the source as the closest mic is, so multiple mics in a room do not cancel each other out. It matters less once Speak AI is analyzing the recording, since the engine is matched to the file you hand it, but clean placement still gives it more to work with.
It depends on the recording. Speak AI routes each file across multiple speech engines and picks the one suited to the audio, language, and accent, rather than forcing every file through one model, so accuracy holds up on mixed microphones and mixed rooms.
Not exactly. Microphones capture sound; Speak AI is the layer that turns the capture, whatever hardware it came from, into a transcript with tone, sentiment, and structured fields. A better microphone gives the AI more to work with, but the accuracy comes from the transcription and analysis layer, not the hardware alone.
There is not one mic that wins every room. The closest thing is matching the mic to the setup: a headset or lavalier for calls, a room or ceiling array for conference rooms, a shotgun or handheld for the field, all feeding into a transcription engine that adapts to whichever one you used.
Enterprise builds support BAAs, custom data processing agreements, SSO, and data residency options. We share security documentation on request and scope each build to your requirements.
From a mic swap to a transcript you trust.
Book a free consult, bring a real recording from your current setup, and watch it transcribed and scored before the meeting ends. Consults include early access to new features, an extended trial, and implementation credits.
English 
Español 
Français 
Italiano 
العربية 
Português do Brasil 
Nederlands 
Deutsch 
עִבְרִית 
Русский 
日本語 
Polski 
Čeština 
Українська 
Slovenščina 
Svenska 
Ελληνικά 
Norsk bokmål 
简体中文 
Türkçe 
Català 
Magyar 
한국어 
Slovenčina 
Suomi