Boston-based voice intelligence startup Modulate has raised $25 in new funding for its platform that uses an array of small models to offer enterprises transcription, emotional analysis, deepfake and AI music detection, and policy enforcement for voice agents in regulated industries.

The funding follows a popular trend among investors in the growing voice AI industry: backing companies that are trying to make AI voices sound more human.

It also rivals other companies trying to detect the intent behind human conversation by analyzing it, and those trying to protect people and companies from deepfake calls, as it is now easy to clone voices.

Modulate’s new funding was led by Future Ventures with participation from Hyperplane and Lakestar. Data from PitchBook indicated that the startup had raised $41 million in funding at a $170 million valuation prior to this round.

The startup was founded in 2017 by Mike Pappas and Carter Huffman, who met as MIT physics undergrads. In its early days, the company focused on providing voice modulation for gaming. But later, it started to concentrate on a voice-based moderation tool.

With the onset of voice AI models, the company is now concentrating on detecting different sorts of AI audio generation and analyzing 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,” Huffman said on a call with TechCrunch.

The company today runs more than 100 models that are largely categorized into two sections: Signal extraction models to understand vocal emotion, tone, language, and synthetic voice determination; and Analysis/detection models that look at intent, like what the customer is trying to say, whether the caller is violating rules, or whether they are trying to scam the receiver.

Huffman said that because it runs smaller models, the company doesn’t need specialized hardware and a ton of compute, which could be crucial when token bills go up. Plus, it’s easier for the company to train models with newer capacities, add them to the lot, and have an orchestrator call them when needed.

Modulate has a varied customer base, but it specializes in deepfake detection and alerting organizations like call centers to a possible scam. It also monitors how AI agents respond to customers to assess the quality of calls, along with making sure that AI follows compliance rules in regulatory areas. Because of these products, Module often sits beside the voice stack being used by a company just to analyze calls.

As more enterprises adopt AI-powered customer service, it is becoming important for them to know why a customer call was a success or a failure. In that case, gauging customers’ intent and response becomes critical beyond basic analysis. Huffman said Modulate can give granular data to enterprises around that.

“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 company said that its tech is also being used to monitor cyberattacks through voice calls.

The startup currently has 40-45 employees and aims to add 10 more people in the coming months to bolster model building. Modulate is currently working on increasing its on-premises and on-device deployment capabilities for increased privacy.

ElevenLabs launched two new speech models on Monday, called ElevenLabs v4 and v4 Turbo, offering more expression control, lower latency for voice agents, and support for more than 90 languages.

The company released its v3 model last year, and teased the model at an event in Warsaw earlier this year. For the v4 generation of models, ElevenLabs is adopting a new architecture that allows for better control and faster cloning. The company said that with v4, users will be able to clone a voice with just 10 seconds of audio.

On the creative side, the model handles voice identity better over longer chunks of text. It also keeps the context of the text in mind while reading it aloud to change expressions. ElevenLabs introduced inline tags to define expression with v3, and is expanding those tags in v4, letting users stack multiple tags and having the model follow the sequence.

The previous version supported 70 languages, and ElevenLabs has worked to get that number up to 90 languages with the new version. The startup said that it observed the biggest quality jump in Japanese, Brazilian Portuguese, Mandarin and Cantonese.

ElevenLabs has scaled its enterprise calling business rapidly over the last year, with more than 55% of its business coming from large companies. The company said that the new model is suited for voice agents, as the new version has lower latency to allow for more fluid conversation. Plus, the v4 can start generating audio as soon as the LLM behind it starts generating answers. What’s more, the model can also handle confrontations, escalations, and holds differently for better issue resolution.

Competition in speech models has ramped up as startups like Cartesia, Deepgram, Fish Audio, Boson, and WellSaid Labs have created expressive speech models. Big companies like Google and OpenAI have also improved their voice models.

ElevenLabs raised $500 million earlier this year from Sequoia earlier this year, in a round led by Sequoia that valued the company at $11 billion. There are already rumors of a followup fundraising round that would value the company at $22 billion. ElevenLabs annualized revenue run rate has climbed from roughly $330 million at the start of the year to over $600 million. The company has aggressively hired personnel in different markets such as India, Europe, and Brazil, as its headcount has reached over 800.

In a recent interview with TechCrunch, the company’s co-founder and CEO Mati Staniszewski said that the company is aiming for an IPO “in the next years,” but didn’t commit to a timeline.

It’s only been a month since AI assistant startup Instinct announced a fundraise that valued it at $2.5 billion, and now the company has already raised another $1 billion, from investors including Sequoia Capital, Benchmark Capital and Coatue, valuing the company at $10 billion.

The news of the company’s fundraising efforts was reported earlier this month by The Information. In a press release on Monday, Instinct confirmed this was a Series C round — a pretty quick growth round for a startup that launched its invite-only service in August 2026.

The quick fundraises demonstrate the fervor around a new class of consumer AI agents, which can not only answer questions and engage in conversations, but can actually get things done for their users, whether that’s booking travel plans or restaurant reservations, making purchases, paying bills, canceling subscriptions, conducting tedious research, ordering groceries, and more.

When asked to perform a task, Instinct uses its own phone number and computer. The company recently rolled out other new features, like “concierge: that can make phone calls for you, to manage things like making appointments at places that don’t offer online booking, as well as a “trusted person network” which allows one person’s Instinct agent to coordinate plans with their friends’ agents.

However, these capabilities come at a cost: Some users are questioning the amount of personal information they have to disclose to AI agents to gain access to such capabilities. Instinct’s initial version of its privacy policy was particularly worrisome due to its overreach. The policy has since been updated.

Despite its AI assistant’s viral adoption, Instinct is now facing fresh competition from Meta’s own AI assistant, Muse, which offers many similar features, plus a system that can deeply integrate with Meta’s social products. That means Muse can do much of what Instinct does, as well as tasks like monitoring and summarizing your Instagram DMs or Facebook Groups, keeping an eye on Marketplace listings, and more.

Such capabilities have sent Muse to the top of the U.S. app stores, where it has been downloaded millions of times. Instinct, which uses SMS and texting to communicate with its users, doesn’t have a mobile app yet. The startup has also not shared its user numbers or any growth metrics, but clearly its investors are seeing something they like.

Instinct declined to offer interviews with founder Noah Shinn alongside the fundraising news, but shared a statement attributed to him: “We’re building Instinct to be the best personal agent that can handle the deeply personal nuances of everyday life. This funding helps us bring Instinct to more people and continue building the future of personal AI. It’s an exciting, creative time, and we’re just getting started.”