Modulate raises $25 million for voice models for deepfake detection and compliance monitoring
Startup Modulate has raised $25 million to develop voice models for detecting deepfake voices, analyzing emotions, and monitoring compliance of AI agents in regulated industries.
Boston-based startup Modulate has raised $25 million in a new funding round led by Future Ventures, with participation from Hyperplane and Lakestar. According to PitchBook data, prior to this round the company had raised a total of $41 million at a $170 million valuation. Modulate was founded in 2017 by Mike Pappas and Carter Huffman, who met as physics students at MIT. The company originally offered voice modulation for the gaming industry, later pivoting to tools for moderating voice communication.
According to the company, it now operates more than 100 models divided into two categories: signal extraction models (recognizing emotion in voice, tone, language, and determining whether a voice is synthetic) and analysis and detection models (speaker intent, policy violations, fraud attempts). According to co-founder Carter Huffman, using smaller models means the company doesn't need specialized hardware or significant computing power, which makes it easier to add new models and orchestrate them as needed.
The company targets in particular the detection of deepfake voices and alerting call centers to possible fraud, as well as monitoring the quality of calls handled by AI agents and compliance with regulatory rules in regulated industries. According to the company, its technology is also used to monitor cyberattacks carried out via voice calls. Modulate currently has 40-45 employees and plans to hire approximately 10 more people in the coming months for model development, focusing on expanding on-premises and on-device deployment options for privacy reasons.
Why it matters
For companies operating call centers or AI voice agents in regulated industries (finance, healthcare, etc.), there is a growing specialized tool that goes beyond standard transcription - according to the company, it can detect fraudulent or cloned voices and more subtly assess customer satisfaction even in cases where the customer remains polite but dissatisfied. This is particularly relevant for compliance and risk teams that need to document adherence to regulatory rules in automated customer communication.
Relevant practical impact
What this means
For a business
Companies operating call centers or AI voice agents in regulated industries gain another vendor of tools for detecting deepfake calls, monitoring compliance, and evaluating call quality beyond basic transcription.
Risks and complianceCheck the original
Event sources
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