Meet the Innovators

Our team combines decades of research in AI, musicology, and signal processing from the University of Oslo's world-renowned music technology department.

Dr. Olivier Lartillot

Dr. Olivier Lartillot

Chief Technology Officer & Co-Founder

Leading researcher at UiO's RITMO Centre for Interdisciplinary Studies in Rhythm, Time and Motion and professor at the MishMash Center for AI and Creativity. Pioneered the hybrid transformer-rule system approach that forms the core of our transcription technology. Over 20 years of experience in computational musicology and MIR (Music Information Retrieval).

Lars Løberg Monstad

Lars Løberg Monstad

Chief Executive Officer & Co-Founder

Lars is a highly skilled Machine Learning Engineer and Full-Stack Developer specializing in music information retrieval. He has published several articles in the field and developed advanced AI models for music analysis and transcription. As CEO and Co-Founder, he drives the company's vision and product strategy, combining deep technical expertise with entrepreneurial leadership.

Karstein Grønnesby

Karstein Grønnesby

Chief Operating Officer

Karstein holds a Master's in Music Technology from the University of Oslo and brings years of experience coordinating projects and productions in the music sector. He oversees project management and leads the B2B side of our product, drawing on his background from Samspill International Music Network and Norsk Viseforum.

Our Mission

We believe that music transcription should be accessible to everyone - from students learning their favorite songs to professional musicians preserving their compositions. Our AI technology bridges the gap between audio and notation, making sheet music creation as simple as recording a performance.

Innovation & Research

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Hybrid AI System

Combining transformers with music theory rules for unmatched accuracy

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Ethical Training

100% public domain and licensed recordings - no copyright concerns

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Privacy First

Your recordings are never used for training - complete data isolation

Supported by the Norwegian Research Council and the University of Oslo