Lattica, a cloud-based platform pioneering the secure and private use of AI through Fully Homomorphic Encryption (FHE), has emerged from stealth with fresh backing to drive its ambitious plans.
The company announced a $3.25m pre-seed funding round, led by Konstantin Lomashuk’s Cyber Fund, with additional participation from angel investor Sandeep Nailwal, co-founder of Polygon Network and Sentient: The Open AGI Foundation, among others.
Lattica’s technology is designed to establish a critical new standard for sectors such as healthcare, finance and government, where concerns around data privacy and security have slowed AI adoption. Its platform leverages FHE to ensure all communications between AI providers and end users remain encrypted throughout the process, eliminating the need for decryption.
With the new capital, Lattica plans to further develop its FHE-based platform, enhancing secure data processing capabilities for industries handling sensitive information. It also intends to scale its offering across a wide range of hardware infrastructures, making secure AI practical and accessible.
Lattica’s founder and CEO, Dr. Rotem Tsabary, holds a PhD in lattice-based cryptography from the Weizmann Institute of Science. The company’s innovation lies in its Homomorphic Encryption Abstraction Layer (HEAL), a cloud-based service that boosts FHE performance and standardises acceleration across GPUs, TPUs, CPUs and specialised hardware like ASICs and FPGAs. As part of its emergence from stealth, Lattica has also released demos and published findings from an FHE community survey, which confirmed a growing belief that hardware and software integration is key to making FHE commercially viable.
Lattica founder and CEO Dr. Rotem Tsabary said, “By combining the advancements of hardware acceleration with software-based optimization, we realized that not only could we improve FHE efficiency to the point of commercial viability, but use it to solve critical data dilemmas holding back AI’s adoption in sensitive industries. We’re enabling practical FHE by developing a solution that is tailor made for neural networks.”
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