Hamming.ai, the AI voice agent reliability platform, announced that it has raised $3.8 million in seed funding led by Mischief, with participation from YCombinator, AI Grant, Pioneer, Coalition Operators, Coughdrop and angels including Hiten Shah, Ran Makavy, Max Kolysh, Richard Aberman and Kulveer Taggar.
“Conversational AI is advancing rapidly —but most testing and governance tools haven’t caught up to match developer needs and compliance realities,” said Lauren Farleigh, Co-Founder and GP at Mischief. “Hamming.ai will be foundational to ensuring this technology advances safely, as AI reshapes interactions between businesses and their customers.”
People make billions of calls a day. As conversational AI advances, analysts like Forrester predict that the majority of calls will be handled by AI, built by thousands of companies tackling every single vertical. Yet making AI voice agents reliable is hard. A small change in prompts or model providers can cause large changes in voice agent responses. Engineers spend hours a day testing their agents by hand — a slow, ad hoc, and often inadequate process. Even when these voice agents are launched to production, operations teams then listen to 1000s of calls per day to discover edge cases with their voice agents that were missed by their manual testing methods. This makes such voice AI systems costly to set up and, due to deficient testing infrastructure, potential sources of liability and adverse public attention.
Founded in 2024, Hamming automates the testing, monitoring, and governance for AI voice agents. Deploying its own AI voice agents that act like real people, Hamming.ai can place thousands of phone calls to client voice agents simultaneously. Hamming.ai also provides LLM prompt management solutions for B2B teams; automated AI voice agent red-teaming to detect vulnerabilities and call analytics solutions to track how users are engaging with AI voice agents in production and to flag cases in need of attention. Hamming.ai’s approach is 20 times faster and 10 times cheaper than having humans test AI voice agents manually; it aims to further improve these margins through its additional product development and refinement.
Co-founders Sumanyu Sharma and Marius Buleandra come to Hamming.ai with experience building trust and safety infrastructure at Citizen, the Founders Fund-backed personal safety network, where they met in 2019. Sharma helped quadruple Citizen’s user base as its Head of Data. Prior to Citizen, he grew an AI-powered sales program at Tesla to 100s of millions in revenue per year as a Senior Staff Data Scientist, and researched AI-powered medical image search at the University of Waterloo, Canada. He serves as Hamming’s CEO. In addition to his time at Citizen, Buleandra comes to Hamming.ai with experience in data infrastructure, AI, and complex systems engineering at Anduril, Square, and Microsoft, and was a founding engineer at Spell, a machine-learning observability and infrastructure startup acquired by Reddit. He serves as Hamming.ai’s CTO.
“If 2024 was the year of the prototype, 2025 will be the year of reliability,” Sharma said. “Industries like medicine, law, insurance, and real estate are governed by compliance frameworks that govern how businesses interact and share information with their clients. As these regulations catch up to the realities of AI, automated testing will be a cornerstone to every long-term and trust-focused AI strategy.”
About Hamming.ai
Founded in 2024 by Sumanyu Sharma and Marius Buleandra, Hamming.ai provides AI developers automated experimentation, prompting, and call analytics tools to ensure voice AI agent reliability and resilience. Deploying its own AI voice agents that act like real people, Hamming.ai can place thousands of phone calls to client voice agents simultaneously, identifying bugs more expediently and efficiently than current manual testing processes allow. Hamming.ai also provides LLM prompt management solutions for B2B teams; automated voice agent red-teaming to detect vulnerabilities and call analytics solutions to track how users are engaging with AI voice agents in production and to flag cases in need of attention.
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