Boston-based voice intelligence startup Modulate has raised $25 million to expand its suite of voice models and analysis tools. The capital will support the company’s platform, which deploys an array of smaller models to deliver transcription, emotional analysis, deepfake detection, and AI music identification for enterprises. It also enforces policy rules for voice agents operating in regulated sectors.
The funding trend
Investors are increasingly backing firms that attempt to make artificial voices sound more human. This move places Modulate alongside competitors analysing the intent behind human speech and protecting organisations from cloned voices.
Future Ventures led the round, with participation from Hyperplane and Lakestar. PitchBook data shows the startup had previously raised $41 million at a $170 million valuation.
Founders and origins
Mike Pappas and Carter Huffman founded the company in 2017 after meeting as MIT physics undergraduates. Early work focused on voice modulation for gaming before shifting to a voice-based moderation tool.
With the arrival of voice AI models, the business now concentrates on detecting various forms of AI audio generation and analysing the intent behind a person’s words.
“Our insight into the voice AI space is that a lot of folks are doing transcription, but there’s not really any capability out there that gets the full nuance and full understanding of a conversation, which is so important when you’re talking to another human being,” Carter Huffman said during a call with TechCrunch.
How the models work
The company currently runs more than 100 models split into two categories. Signal extraction models understand vocal emotion, tone, language, and synthetic voice determination. Analysis and detection models examine intent, such as what a customer is trying to say, whether they are violating rules, or if they are attempting a scam.
Huffman noted that running smaller models means the company avoids the need for specialised hardware and massive compute power. This approach matters as token bills rise. It also allows the team to train models with newer capacities, add them to the collection, and have an orchestrator call them when needed.
Who uses the tools
Modulate serves a varied customer base but specialises in deepfake detection and alerting call centres to possible scams. It also monitors how AI agents respond to customers to assess call quality and ensures AI follows compliance rules in regulated areas. These products often sit beside the voice stack used by a company simply to analyse calls.
As more enterprises adopt AI-powered customer service, understanding why a call succeeded or failed becomes important. Gauging intent and response is critical beyond basic analysis. Huffman said Modulate provides granular data to help companies with this.
“I think when companies think of emotion analysis, they think if the customer was neutral or positive, the call was a success, and if the customer was negative, the call was a failure. But actually, many times people will be polite even to, like, AI agents or bots. Right. And they won’t come across as angry, but they’ll be very dissatisfied,” he said.
The startup also uses its technology to monitor cyberattacks through voice calls.
What it means
Modulate currently employs 40 to 45 staff and plans to hire 10 more people in the coming months to support model building. The company is working to increase on-premises and on-device deployment capabilities to improve privacy.
