The new funding, announced Monday (Sept. 28), comes as the company is seeing its artificial intelligence (AI) models increasingly used in areas like fraud prevention, voice AI agent supervision, trust and safety, and customer experience.
“Voice is becoming a primary interface for AI, and that creates a whole new set of problems that can’t be solved from a transcript,” Carter Huffman, Modulate’s co-founder and CEO, said in a news release.
“We’re already using audio-native AI to protect organizations from deepfake attacks, help voice agents understand emotion and respond with more empathy, identify dangerous behavior in online conversations, and monitor whether voice agents are actually performing the way they’re supposed to.”
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Beyond that are more than a hundred specialized models working in tandem to “understand what’s really happening across audio, with dramatically less cost and compute than traditional large models,” Huffman added.
The release says Modulate’s models now analyze more than 10 million hours of audio every month, recently exceeding 600 million hours of total audio processed. The company says its models are used for things like guarding healthcare institutions from deepfake hackers, preventing child grooming voice conversations and reducing “extremism and harassment on social platforms.”
“Modulate is now expanding the team and infrastructure needed to meet growing demand from developers and partners building voice applications across security, customer experience, communications, AI agent supervision, and trust and safety,” the release added.
The new funding comes as fraudsters increasingly use AI-created voices and likenesses to carry out their crimes. The FBI’s 2025 Internet Crime Report found more than 22,000 AI-related fraud complaints with losses of more than $893 million, while congressional researchers estimate that the vast majority (more than 95%) of voice clone victims don’t report their losses.
Research from PYMNTS Intelligence shows that 58% of companies with upwards of $1 billion in annual revenue reported running into AI-generated documents or deepfake-related attacks in the past year, a full 11 percentage points more than smaller businesses.
In another report this year, PYMNTS examined the rise of fraud cases that combine deepfake video, cloned voices, synthetic identity creation, invented employment histories and AI-generated financial behavior into one engineered persona.
“These synthetic borrowers are not merely fake identities in the traditional sense,” the report said. “They are algorithmically optimized consumers designed to survive onboarding checks, satisfy underwriting models and disappear once loans are funded.”
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