Tarini Padmanabhuni founded DetectifAI after a personal incident in which her grandfather was tricked by a phone call. The caller’s voice claimed to be his brother, said he had been kidnapped and demanded a ransom; the grandfather paid and later learned the brother had been elsewhere and unaware. The voice was an AI-generated deepfake. “What stayed with me wasn’t the money,” Padmanabhuni says. “It was that he had no way of telling.”
That episode, roughly two years ago, motivated Padmanabhuni to build a company aimed at preventing similar scams by detecting synthetic voices directly on users’ devices. DetectifAI is based in San Francisco and focuses on running compact AI models inside smartphones so audio never has to leave the device for analysis.
Why on-device detection matters
The scale of AI-driven voice fraud is growing. According to the FBI, Americans lost nearly $900 million to AI-driven scams last year, a 24 percent increase from 2024. People aged 60 and older lost twice as much as those aged 50 to 59.
Padmanabhuni argues that many existing deepfake-detection tools run in the cloud on remote servers, which prevents phone makers from embedding detection as a built-in operating system feature. That leaves the person receiving a call with limited defenses at the moment of attack.
DetectifAI’s approach
Instead of shrinking large cloud models to run on phones, DetectifAI designs small models from the outset that fit within a smartphone’s OS and can deliver an instant determination of whether a voice is AI-generated during calls, voice messages, and other audio. The company’s design ensures the audio does not need to leave the device for detection.
The product is offered as a software development kit (SDK) that phone manufacturers can integrate into their products and license through existing channels. Padmanabhuni compares the potential market effect to AT&T’s exclusive role in the launch of the original iPhone: the first phone maker to ship DetectifAI built in could gain a competitive edge, and she expects deepfake detection to become a standard phone specification like camera resolution.
Business traction and customers
DetectifAI is selling primarily to phone manufacturers, and Padmanabhuni says a secondary revenue stream will come from licensing the technology to businesses and fraud-prevention firms. The startup already records early revenue and handles more than 100,000 calls per month for financial institutions in India. In those deployments, AI voice agents place calls for debt collections and loan follow-ups, with deepfake detection and speaker verification enabled on every call. She declined to name customers because of confidentiality agreements.
Competition and funding
The company operates in a field with several competitors, including Reality Defender, Pindrop, Resemble AI, Microsoft Azure AI Content Safety and Nuance (also owned by Microsoft). DetectifAI’s differentiator is the emphasis on compact, on-device models.
DetectifAI has raised a small seed round from investors Josh Constine (formerly an editor at TechCrunch) and Manohar Kamath, a principal at consulting firm KM Growth. The startup was selected by TechCrunch’s editorial team to compete in the Startup Battlefield at TechCrunch Disrupt, scheduled for Oct. 13–15, 2026, in downtown San Francisco.
Founder background and user testing
Padmanabhuni says she began working in machine learning at age 12 and studied cyber-physical systems at Manipal Institute of Technology in India. There she became, she says, the youngest team lead of India’s first driverless racecar division in Formula Student, an international student engineering competition.
One early product milestone was a small WhatsApp beta test in which users forwarded suspicious voice notes and received an assessment of whether they were real. One tester whose relatives had been scammed told her they would gladly pay for the service. “My grandfather didn’t have that,” Padmanabhuni notes.
By embedding detection in the device operating system, DetectifAI hopes to fill a gap in protection against voice-based deepfake scams and make on-device verification a standard feature in future smartphones.
