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a-Gnostics | Di-agnostics | Pitch Deck

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Shazam for industrial equipment:
the best enterprise app for
sound analytics

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Value proposition
Did you know that: The average cost of unplanned
downtime in lost revenue, financial penalties, idle sta...

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How Di-agnostics works
in Production
Record the sound of how the equipment
works and transmit data via the special
mobile ...

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a-Gnostics | Di-agnostics | Pitch Deck

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Industrial AI sound analytics SaaS for prediction of failures in heat engines and electrical motors, to help enterprises simplify their work and reduce maintenance costs.

Industrial AI sound analytics SaaS for prediction of failures in heat engines and electrical motors, to help enterprises simplify their work and reduce maintenance costs.

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a-Gnostics | Di-agnostics | Pitch Deck

  1. 1. Shazam for industrial equipment: the best enterprise app for sound analytics
  2. 2. Value proposition Did you know that: The average cost of unplanned downtime in lost revenue, financial penalties, idle staff time, and restarting lines is $532K according to senseye. Problem: Industrial equipment breaks. Solution: • Di-agnostics is a service for industrial equipment failure prediction and anomaly detection. Industrial AI sound analytics SaaS for prediction of failures in heat engines and electrical motors, to help enterprises simplify their work and reduce maintenance costs. • It uses Machine Learning and Artificial Intelligence to predict failures before they happen. • The current focus is on sound analytics of heat engines and electrical motors.
  3. 3. How Di-agnostics works in Production Record the sound of how the equipment works and transmit data via the special mobile application. Process it in the cloud infrastructure for data computing and analytics. Use a large dataset of the different sounds of industrial equipment. It was built a comprehensive data pipeline to easily train, schedule retrain, and use specific models. Receive motor status in seconds and analyze spectrogram via UI for administrators and engineers.
  4. 4. 01 02 Di-agnostics advantages No needs to buy additional vibro- or noise- sensor for each electrical engine. Data is the sound. The user stays on the field and sends data for analytics to the cloud. No needs to move to fixed working place. 03 No needs to teach new engineer how to hear if the equipment works in normal or pre-failure mode. 04 a-Gnostics already has production implementation of Energy Consumption Forecasts service for large Ukrainian agroindustrial holding MHP, 30+ daily enterprise users. Di-agnostics uses existing infrastructure, machine learning models, AI and ML, and data pipelines. More than 1,500,000 forecasts in the system.
  5. 5. Initial focus on global compound feed industry, # of feed mills:– 28,414 in the world; – 6,232 in the USA; – 6,948 in Europe. 3.6 million professional truck drivers in the U.S. and 37.9 million trucks. There are 1,000 grain elevators in Ukraine; 8,800 in the USA; 28,000 in the world. Poultry: slaughter 68,000,000,000 heads a year vs 403,000,000 (0.6%) at MHP, largest in Ukraine. The biogas market will grow from $3.1 to $6.52B in 2028. In 2022 Di-agnostics is piloting in Ukraine at: 1. compound feed manufacturers and grain elevators; 2. poultry farms; 3. biogas stations. Implementations and go2market In the United States and Canada pre-piloting: 1. oil & gas industry, specifically for electrical engines at rod pumps, and other equipment. The next focus is on internal combustion engines for trucking industry. During the tests accuracy of the models – 93% and above in specific cases.
  6. 6. Market overview Total market of electrical motors: $106.3 B in 2020, and reach $207.3 B by 2030; CAGR of 6.7%. Industry: agritech, 4.3%; repair and maintenance budget, 5%. • TAM (Total Addressable Market): 121 B x 0.043 = 5.2 B engines in agro. • SAM (Serviceable Addressable Market): 5.2 B x 0.05 = 260 M spent for motors repair. • SOM (Serviceable & Obtainable Market, Share of Market): 260 M, 10% of enterprises, 26 M / Year. Trucking industry & heat engines: $732.3 billion in gross freight revenues. • Top 20 electric motor manufacturers and 300 million industry globally; Over 30 million engines sold each year. • The farms equipment market is 99.4B in 2021 and $126 in 2027. • The predictive maintenance market is $4B in 2020 and $18.6B in 2027. • 50,000 employees work in repair and maintenance services in the USA.
  7. 7. Competitors Features: 1 – Using AI ML, cloud portal, real-time. 2 – Special app vs hardware. 3 – Accuracy. Di-agnostics Neuronsoundware Signally.ai Groundup.ai NLacoustics 1 ✔️ ✔️ ✔️ ✔️ ✔️ 2 App Sensor + Hardware Data Data + Hardware Camera 3 93%+ Depends on data problem Depends on data problem Maintenance cost reduce by 25% No public info 4 Automatically* 3 month No public info Enterprise specific 3-5 month 5 Scalable to all electrical motors Industry task specific Industry task specific Industry task specific Industry task specific 6 B2E SaaS** and B2C*** B2E B2E B2E B2E * – Companies and users do self-registration, and models are retrained automatically as new data comes. ** – Framework is ready for unlimited scaling in the cloud. *** – There are many users’ apps on sound or noise diagnostics; no backend and enterprise-level security. 4 – Time to set-up new customer; new model. 5 – Is specialized in equipment industry? 6 – Enterprise vs B2C. Initial investment.
  8. 8. Roadmap and finances $150,000 for 10% of current shares. 1. 0 – cloud. The current grant from Microsoft for $150,000 for 2 years on Azure. 2. 30% – business development. A salesperson in Houston, TX; UK or Germany. 3. 10 % – marketing. Customers’ success stories, upgrade existing materials and create a video. 4. 10% – advertising. Launch an online campaign. Focus on feed mills in U.S.; trucking industry. 5. 50% – product development. According to the roadmap. Technical and product roadmap for 2022, September – December: 1) prediction on the phone, offline; 2) models improvements; 3) iOS version; 2023: 4) Remaining Useful Life (RUL); 5) security certification. In late 2021 it was released the Enterprise version of Di-agnostics service, the cost is $2,500 per month per enterprise, to monitor up to 25 large engines (more than 200 kW).
  9. 9. Yaroslav Nedashkovskyi СТО, Co-founder. System architect, technical team lead, and senior software developer since 2003. Joined SoftElegance in 2011. Competences: Architecture, Technical implementation, Machine Learning and AI. a-Gnostics co-founders and team Andriy Stolbov Chairman of the board, Co-founder. Founder of SoftElegance, a custom software development outsourcing company, 1993. Competences: Strategy, Finances. Andrii Starzhynskyi CEO, Co-founder. In software development and project management business since 2004. Joined SoftElegance in 2010. Co-founders work together since 2011. The first service of a-Gnostics launched in 2018, after graduation from Energy Accelerator. The current team that works on sound analytics since December 2021, consists of 6 members. Competences: Product, Marketing, Sales.
  10. 10. S E Project Management Ltd. Company number 07602794 142 Cromwell Road, London, United Kingdom, SW7 4EF +44 207 048 7339 Andrii Starzhynskyi andrii.starzhynskyi@a-gnostics.com +380 50 312 4725 www.facebook.com/agnosticscom www.linkedin.com/company/agnostics www.a-gnostics.com Contacts

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