Using SOTA techniques, we're now able
to accurately predict exact molecular structures of NOVEL compounds directly from mass
spectra—INDEPENDENT of instrument type.
Future developments may incorporate additional modalities,
such as FTIR, to enable multimodal data fusion and further enhance structural inference.
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Expert chemist-level molecular insight
Our AI ingests SMILES data to assemble a complete molecular dossier—substructure, functional groups,
receptor-binding profiles, and key physicochemical metrics—leveraging this rich information to pinpoint the
true identity of novel compounds.
Alex, Kartik, and Alina founded Revanov in 2024 after witnessing firsthand how legacy government contractors were being outpaced
by agile private-sector innovation. With backgrounds from Purdue, and experience within Anduril, Teledyne, Eli Lilly, and General Motors, we
set out to reimagine CRBNe threat detection. Revanov is bringing
cutting-edge machine learning to bear on one of the most urgent challenges in modern defense.
