A physics-based AI tool for underground characterization from seismic data, giving oil and gas and geotechnical operators a validated go/no-go signal before drilling — in an industry where over 75% of exploration wells fail.
A physics-based AI tool for underground characterization, targeting oil and gas exploration and geotechnical applications onshore and offshore. It uses pre-stack seismic data — physics-driven feature extraction combined with AI classification — to identify hydrocarbon traps, assess geotechnical properties, and produce go/no-go drilling recommendations, in an industry where over 75% of exploration wells fail. Its differentiator is a pre-trained go/no-go model built directly on pre-stack data.
PhD in electro-mechanically coupled materials, Technion (2010). CEO of SEAL — Hydrographic and Geophysical Survey since 2011; previously CEO of the Coastal & Marine Engineering Research Institute at the Technion (2013–2018) and technical manager at Oceana Marine Research (2009–2011).
BSc in oceanography, Hebrew University; Cat-B hydrographic surveyor; COO of SEAL since 2014, with prior roles in hydrographic survey management.
R&D and product manager in an IDF technology unit (2010–2016); co-founder and CEO of JEWELS (3D modelling and digital visualization, 2016–2017) and of Verto.AI (AI for 3D reconstruction, 2018–2025); since 2025 focused on grants and non-dilutive fundraising strategy for startups.
