Draft:V2M

V2M is a technology company specializing in the development of advanced methods utilizing artificial intelligence (AI) and multilayer neural networks to detect faulty sound patterns in vehicles. The company's innovative approach enables the diagnosis of vehicle faults even in challenging dynamic conditions and amidst excessive extraneous noise. V2M holds a patent for its development, and its founder, Peter Bakulov, has contributed to the field with his scientific article "Acoustic Fault Trace as a Diagnostic Parameter of Modern Vehicles ," which was included in the scientific abstract and citation database Scopus in 2022.

History
Founded in 2012 by Peter Bakulov, a former professor at MADI with extensive experience in the automotive industry, V2M aimed to address the issue of vehicle malfunctions that could lead to accidents. Bakulov recognized the potential of recognizing vehicle noises to detect and prevent malfunctions. In 2016, V2M developed a laboratory sample solution to tackle this problem. After five years of development, the company successfully completed a prototype, validated by Bakulov's PhD thesis. V2M's first test vehicle, a Tesla Model 3 Standard Range Plus, was acquired to install the prototype , showcasing the company's potential for partnerships, particularly with technologically advanced entities like Tesla.

To support its growth as a startup, V2M participated in the acceleration program of Starta Ventures. In early 2022, the company secured $100,000 in investment through a SAFE (Simple Agreement for Future Equity).

Developments
V2M has developed an AI technology-based platform that utilizes acoustic sensors, a control unit, and specialized server software to detect vehicle malfunctions through sound analysis. The platform collects and processes sound streams in real-time to diagnose various critical components of a vehicle, including the engine, transmission, bearings, and suspension parts. With an algorithm that periodically checks sensors for safe operation and enables the addition of new features, V2M's technology offers predictive diagnostics, foreseeing and preventing potential failures.

Applications
Beyond automotive applications, V2M's methodology demonstrates versatility and readiness for diverse industries, such as mineral resource extraction, specialized machinery, and commercial vehicle fleets. The technology's ability to detect mechanical or operational irregularities based on auditory cues makes it applicable across various sectors.