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We just raised $25M in new funding led by Future Ventures (@VenturesVc), with @HyperplaneVC and @lakestarvc - bringing Modulate’s total funding to $60 million.
Building the next phase of audio-native AI: more models, more developers, more of the stack that actually understands voice.
Where we stand today:
- Ranked #1 on Hugging Face for deepfake detection and transcription
- 600M+ hours of audio analyzed
- 10M+ hours analyzed every month
A foundational layer of the AI stack is getting more powerful. More soon.
AI agents are starting to make phone calls. What happens when a business refuses to talk to them?
This Tuesday, join Modulate for a LinkedIn Live on detecting AI callers and routing humans to humans, bots to bots.
Join us: bit.ly/47njIOu
Arabic. English. Arabic again.
When a conversation switches languages, Modulate Transcribe detects the language of every utterance, so each turn comes back in the language actually spoken.
→ 100 languages, Arabic included
→ Speaker labels that hold across language switches
→ Batch and streaming
Read the docs and grab 1000 free API credits here: shorturl.at/oHgKp
Clinical audiology sessions are messy 👀
Overlapping speakers. Emotional nuance flat transcripts miss. Notes that need to be instant, not "eventually."
One developer needed transcription that could keep up.
So they built their AI Scribe SaaS on Velma 2.
The result:
👉 Live diarization
👉 Emotional context per utterance
👉 Clinical notes generated in real time
Their words, not ours:
⭐ 5/5: "Accurate Transcriptions with Emotional Context at a Fantastic Price."
This is what audio-native voice intelligence looks like in production 💯
Start building with our APIs. 1000 free credits on us: shorturl.at/BVWq8
Modulate retweeted
Building voice agents is half the problem.
Knowing what’s actually happening in those conversations is the other half.
In Atlanta for VON, demoing @modulate_ai at VoiceAI LIVE! today alongside @SoundHound and others.
2:40 PM, Room C. Come say hi. I’ll be listening to how you say it.
Last week at #SFTechWeek 🎙️
From a rooftop happy hour with VoiceRun to a hackathon with fellow builders, we spent the week connecting with the people shaping the future of voice AI.
Thanks to everyone who joined us. Until next time, SF!
Modulate retweeted
I used to teach spacecraft to see.
Now I teach software to listen.
@modulate_ai recently raised $25M, led by Future Ventures, to build the listening layer for AI.
Cosmology → JPL → voice. Here’s the path 🧵
Speech recognition = audio in, text out. That's the easy version.
Our CEO and Co-founder @whuffman on why real understanding means reading accent, tone, emotion, and cadence too - not just the words.
We're hiring for people who think this way.
See open roles: hubs.ly/Q04xDKp90
#hiring
Modulate retweeted
Winnowing nuance from noise
Modulate runs over 100 voice-native models to discern emotion, tone, intent, and language, and detect synthetic and deepfake voices (ranked #1 on 🤗).
We just led their seed round; news today: techcrunch.com/2026/09/28/mo…
Product: modulate.ai/velma
We just raised $25M in new funding led by Future Ventures (@VenturesVc), with @HyperplaneVC and @lakestarvc - bringing Modulate’s total funding to $60 million.
Building the next phase of audio-native AI: more models, more developers, more of the stack that actually understands voice.
Where we stand today:
- Ranked #1 on Hugging Face for deepfake detection and transcription
- 600M+ hours of audio analyzed
- 10M+ hours analyzed every month
A foundational layer of the AI stack is getting more powerful. More soon.
Modulate retweeted
🚨 A startup just hit #1 on @HuggingFace for deepfake audio detection.
98.9% accuracy. 30–1000× cheaper than every competitor. Nobody is talking about this.
It's called Velma by @modulate_ai and it changes everything about voice security.
🤝 Paid partnership
Modulate retweeted
🚨 BREAKING: Transcription just got 90x cheaper.
One of the biggest hidden costs for AI startups is Speech-to-Text.
Velma-2 by @modulate_ai just launched: $0.03 per audio hour transcribe.
Here’s why this changes everything for AI founders:
I've built two voice pipelines for two different companies.
They both look like this:
Audio → STT → Clean transcript → NLP → Classify → Act
This works, but there's still a problem I can't solve.
Every time I convert audio to text, I'm keeping the words but throwing away the meaning. Tone, hesitation, sarcasm, and stress are all gone. I have the text, but miss its soul.
The folks at @modulate_ai reached out and showed me how to solve this.
Velma is the voice model that's been running inside Call of Duty and GTA Online to catch toxicity in real time.
This model skips the transcript entirely and works directly on the raw audio. This allows the model to take into account the "invisible clues" other models miss.
It can detect up to 150 invisible clues that none else does!
You can access Velma through an API, and it's ~10x cheaper than pushing audio through an LLM.
If you want to give it a try, use this link to get 1,000 free credits:
modulate.ai/api/velma?utm_so…
Thanks to the team for partnering with me on this post.




















