ODIN
Stage
Seed | AliveAbout ODIN
ODIN is a company that specializes in property management automation in the real estate sector. The company offers a range of solutions for managing commercial properties, including business centers, shopping centers, and logistics parks, providing tools for tasks such as tenant management, maintenance scheduling, and utility tracking. The company primarily serves property management companies and businesses in the real estate sector. It is based in Moscow, Russian Federation.
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ODIN's Product Videos
ODIN's Products & Differentiators
ODIN
ODIN is a client mobile application, a single web portal for employees and tenants, as well as a mobile application for field personnel. Everything from tenant requests, to rental agreements and elevator maintenance plans is in a single window. The property manager knows whether the tenant is satisfied and how the engineers work. ODIN allows you to increase the landlord company's revenue by 2-6% and free up about 500 hours of work of each engineer of the FM/PM company. Right now, ODIN is operating on 9 000,000+ m2 of commercial buildings from Moscow City to Nur-Sultan and Yerevan.
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Expert Collections containing ODIN
Expert Collections are analyst-curated lists that highlight the companies you need to know in the most important technology spaces.
ODIN is included in 1 Expert Collection, including Real Estate Tech.
Real Estate Tech
2,485 items
Startups in the space cover the residential and commercial real estate space. Categories include buying, selling and investing in real estate (iBuyers, marketplaces, investment/crowdfunding platforms), and property management, insurance, mortgage, construction, and more.
ODIN Patents
ODIN has filed 15 patents.
The 3 most popular patent topics include:
- fluid dynamics
- automation
- building automation
Application Date | Grant Date | Title | Related Topics | Status |
|---|---|---|---|---|
1/31/2019 | 6/13/2023 | Fluid dynamics, Sleep disorders, Aerodynamics, Abnormal respiration, Breathing abnormalities | Grant |
Application Date | 1/31/2019 |
|---|---|
Grant Date | 6/13/2023 |
Title | |
Related Topics | Fluid dynamics, Sleep disorders, Aerodynamics, Abnormal respiration, Breathing abnormalities |
Status | Grant |
Latest ODIN News
Aug 3, 2021
by Richard Scott Engineers at the US Naval Surface Warfare Center Dahlgren Division (NSWCDD) have designed an artificial intelligence (AI)-based decision aid designed to assist sailors operating high-energy laser (HEL) weapon systems. An artist's rendering of Lockheed Martin's HELIOS system: one of several first-generation HEL weapons being introduced by the US Navy into frontline service. (Lockheed Martin) The High Energy Laser Fire Control Decision Aid (HEL FCDA) is intended to improve response time and accuracy. Development has been informed by a NSWCDD user performance study to optimise human-machine teaming. The US Navy is currently introducing a first generation of HEL weapons to frontline service. These include the AN/SEQ-4 Optical Dazzling Interdictor, Navy (ODIN), and the 60+ kW class MK 5 Mod 0 High Energy Laser with Integrated Optical-dazzler with Surveillance (HELIOS). Whereas ODIN is designed to dazzle or disrupt the sensors fitted to unmanned aerial systems (UASs), the higher power HELIOS is intended to defeat both small boat and UAS threats. A critical aspect of HEL FCDA development has been optimising the interaction between the human operator and the machine, with the objective to build operator ‘trust' in the system. Sailors participated in a human performance experiment that used a simulated decision aid to collect data about the impacts on kill chain time, trust, neutralisation rate, usability and workload. The simulation – a machine learning (ML) algorithm – became the basis for the HEL FCDA human performance testing to predict the efficacy of the final product. Already a Janes subscriber? Read the full article via the Client Login by Richard Scott Engineers at the US Naval Surface Warfare Center Dahlgren Division (NSWCDD) have designed an artificial intelligence (AI)-based decision aid designed to assist sailors operating high-energy laser (HEL) weapon systems. An artist's rendering of Lockheed Martin's HELIOS system: one of several first-generation HEL weapons being introduced by the US Navy into frontline service. (Lockheed Martin) The High Energy Laser Fire Control Decision Aid (HEL FCDA) is intended to improve response time and accuracy. Development has been informed by a NSWCDD user performance study to optimise human-machine teaming. The US Navy is currently introducing a first generation of HEL weapons to frontline service. These include the AN/SEQ-4 Optical Dazzling Interdictor, Navy (ODIN), and the 60+ kW class MK 5 Mod 0 High Energy Laser with Integrated Optical-dazzler with Surveillance (HELIOS). Whereas ODIN is designed to dazzle or disrupt the sensors fitted to unmanned aerial systems (UASs), the higher power HELIOS is intended to defeat both small boat and UAS threats. A critical aspect of HEL FCDA development has been optimising the interaction between the human operator and the machine, with the objective to build operator ‘trust' in the system. Sailors participated in a human performance experiment that used a simulated decision aid to collect data about the impacts on kill chain time, trust, neutralisation rate, usability and workload. The simulation – a machine learning (ML) algorithm – became the basis for the HEL FCDA human performance testing to predict the efficacy of the final product. Already a Janes subscriber? Read the full article via the Client Login by Richard Scott Engineers at the US Naval Surface Warfare Center Dahlgren Division (NSWCDD) have designed an artificial intelligence (AI)-based decision aid designed to assist sailors operating high-energy laser (HEL) weapon systems. An artist's rendering of Lockheed Martin's HELIOS system: one of several first-generation HEL weapons being introduced by the US Navy into frontline service. (Lockheed Martin) The High Energy Laser Fire Control Decision Aid (HEL FCDA) is intended to improve response time and accuracy. Development has been informed by a NSWCDD user performance study to optimise human-machine teaming. The US Navy is currently introducing a first generation of HEL weapons to frontline service. These include the AN/SEQ-4 Optical Dazzling Interdictor, Navy (ODIN), and the 60+ kW class MK 5 Mod 0 High Energy Laser with Integrated Optical-dazzler with Surveillance (HELIOS). Whereas ODIN is designed to dazzle or disrupt the sensors fitted to unmanned aerial systems (UASs), the higher power HELIOS is intended to defeat both small boat and UAS threats. A critical aspect of HEL FCDA development has been optimising the interaction between the human operator and the machine, with the objective to build operator ‘trust' in the system. Sailors participated in a human performance experiment that used a simulated decision aid to collect data about the impacts on kill chain time, trust, neutralisation rate, usability and workload. The simulation – a machine learning (ML) algorithm – became the basis for the HEL FCDA human performance testing to predict the efficacy of the final product. Already a Janes subscriber? Read the full article via the Client Login by Richard Scott Engineers at the US Naval Surface Warfare Center Dahlgren Division (NSWCDD) have designed an artificial intelligence (AI)-based decision aid designed to assist sailors operating high-energy laser (HEL) weapon systems. An artist's rendering of Lockheed Martin's HELIOS system: one of several first-generation HEL weapons being introduced by the US Navy into frontline service. (Lockheed Martin) The High Energy Laser Fire Control Decision Aid (HEL FCDA) is intended to improve response time and accuracy. Development has been informed by a NSWCDD user performance study to optimise human-machine teaming. The US Navy is currently introducing a first generation of HEL weapons to frontline service. These include the AN/SEQ-4 Optical Dazzling Interdictor, Navy (ODIN), and the 60+ kW class MK 5 Mod 0 High Energy Laser with Integrated Optical-dazzler with Surveillance (HELIOS). Whereas ODIN is designed to dazzle or disrupt the sensors fitted to unmanned aerial systems (UASs), the higher power HELIOS is intended to defeat both small boat and UAS threats. A critical aspect of HEL FCDA development has been optimising the interaction between the human operator and the machine, with the objective to build operator ‘trust' in the system. Sailors participated in a human performance experiment that used a simulated decision aid to collect data about the impacts on kill chain time, trust, neutralisation rate, usability and workload. The simulation – a machine learning (ML) algorithm – became the basis for the HEL FCDA human performance testing to predict the efficacy of the final product. Already a Janes subscriber? Read the full article via the Client Login by Richard Scott Engineers at the US Naval Surface Warfare Center Dahlgren Division (NSWCDD) have designed an artificial intelligence (AI)-based decision aid designed to assist sailors operating high-energy laser (HEL) weapon systems. An artist's rendering of Lockheed Martin's HELIOS system: one of several first-generation HEL weapons being introduced by the US Navy into frontline service. (Lockheed Martin) The High Energy Laser Fire Control Decision Aid (HEL FCDA) is intended to improve response time and accuracy. Development has been informed by a NSWCDD user performance study to optimise human-machine teaming. The US Navy is currently introducing a first generation of HEL weapons to frontline service. These include the AN/SEQ-4 Optical Dazzling Interdictor, Navy (ODIN), and the 60+ kW class MK 5 Mod 0 High Energy Laser with Integrated Optical-dazzler with Surveillance (HELIOS). Whereas ODIN is designed to dazzle or disrupt the sensors fitted to unmanned aerial systems (UASs), the higher power HELIOS is intended to defeat both small boat and UAS threats. A critical aspect of HEL FCDA development has been optimising the interaction between the human operator and the machine, with the objective to build operator ‘trust' in the system. Sailors participated in a human performance experiment that used a simulated decision aid to collect data about the impacts on kill chain time, trust, neutralisation rate, usability and workload. The simulation – a machine learning (ML) algorithm – became the basis for the HEL FCDA human performance testing to predict the efficacy of the final product. Already a Janes subscriber? Read the full article via the Client Login by Richard Scott Engineers at the US Naval Surface Warfare Center Dahlgren Division (NSWCDD) have designed an artificial intelligence (AI)-based decision aid designed to assist sailors operating high-energy laser (HEL) weapon systems. An artist's rendering of Lockheed Martin's HELIOS system: one of several first-generation HEL weapons being introduced by the US Navy into frontline service. (Lockheed Martin) The High Energy Laser Fire Control Decision Aid (HEL FCDA) is intended to improve response time and accuracy. Development has been informed by a NSWCDD user performance study to optimise human-machine teaming. The US Navy is currently introducing a first generation of HEL weapons to frontline service. These include the AN/SEQ-4 Optical Dazzling Interdictor, Navy (ODIN), and the 60+ kW class MK 5 Mod 0 High Energy Laser with Integrated Optical-dazzler with Surveillance (HELIOS). Whereas ODIN is designed to dazzle or disrupt the sensors fitted to unmanned aerial systems (UASs), the higher power HELIOS is intended to defeat both small boat and UAS threats. A critical aspect of HEL FCDA development has been optimising the interaction between the human operator and the machine, with the objective to build operator ‘trust' in the system. Sailors participated in a human performance experiment that used a simulated decision aid to collect data about the impacts on kill chain time, trust, neutralisation rate, usability and workload. The simulation – a machine learning (ML) algorithm – became the basis for the HEL FCDA human performance testing to predict the efficacy of the final product. Already a Janes subscriber? Read the full article via the Client Login by Richard Scott Engineers at the US Naval Surface Warfare Center Dahlgren Division (NSWCDD) have designed an artificial intelligence (AI)-based decision aid designed to assist sailors operating high-energy laser (HEL) weapon systems. An artist's rendering of Lockheed Martin's HELIOS system: one of several first-generation HEL weapons being introduced by the US Navy into frontline service. (Lockheed Martin) The High Energy Laser Fire Control Decision Aid (HEL FCDA) is intended to improve response time and accuracy. Development has been informed by a NSWCDD user performance study to optimise human-machine teaming. The US Navy is currently introducing a first generation of HEL weapons to frontline service. These include the AN/SEQ-4 Optical Dazzling Interdictor, Navy (ODIN), and the 60+ kW class MK 5 Mod 0 High Energy Laser with Integrated Optical-dazzler with Surveillance (HELIOS). Whereas ODIN is designed to dazzle or disrupt the sensors fitted to unmanned aerial systems (UASs), the higher power HELIOS is intended to defeat both small boat and UAS threats. A critical aspect of HEL FCDA development has been optimising the interaction between the human operator and the machine, with the objective to build operator ‘trust' in the system. Sailors participated in a human performance experiment that used a simulated decision aid to collect data about the impacts on kill chain time, trust, neutralisation rate, usability and workload. The simulation – a machine learning (ML) algorithm – became the basis for the HEL FCDA human performance testing to predict the efficacy of the final product. Already a Janes subscriber? Read the full article via the Client Login by Richard Scott Engineers at the US Naval Surface Warfare Center Dahlgren Division (NSWCDD) have designed an artificial intelligence (AI)-based decision aid designed to assist sailors operating high-energy laser (HEL) weapon systems. An artist's rendering of Lockheed Martin's HELIOS system: one of several first-generation HEL weapons being introduced by the US Navy into frontline service. (Lockheed Martin) The High Energy Laser Fire Control Decision Aid (HEL FCDA) is intended to improve response time and accuracy. Development has been informed by a NSWCDD user performance study to optimise human-machine teaming. The US Navy is currently introducing a first generation of HEL weapons to frontline service. These include the AN/SEQ-4 Optical Dazzling Interdictor, Navy (ODIN), and the 60+ kW class MK 5 Mod 0 High Energy Laser with Integrated Optical-dazzler with Surveillance (HELIOS). Whereas ODIN is designed to dazzle or disrupt the sensors fitted to unmanned aerial systems (UASs), the higher power HELIOS is intended to defeat both small boat and UAS threats. A critical aspect of HEL FCDA development has been optimising the interaction between the human operator and the machine, with the objective to build operator ‘trust' in the system. Sailors participated in a human performance experiment that used a simulated decision aid to collect data about the impacts on kill chain time, trust, neutralisation rate, usability and workload. The simulation – a machine learning (ML) algorithm – became the basis for the HEL FCDA human performance testing to predict the efficacy of the final product. Already a Janes subscriber? Read the full article via the Client Login
ODIN Frequently Asked Questions (FAQ)
Where is ODIN's headquarters?
ODIN's headquarters is located at st. Flotskaya, 5, Bldg. BUT, Moscow.
What is ODIN's latest funding round?
ODIN's latest funding round is Seed.
Who are the investors of ODIN?
Investors of ODIN include FRIA Accelerator.
Who are ODIN's competitors?
Competitors of ODIN include HqO and 8 more.
What products does ODIN offer?
ODIN's products include ODIN and 1 more.
Who are ODIN's customers?
Customers of ODIN include SBER (former Sberbank), BOSCH Russia, O1 Properties, Raven Russia and ENKA.
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