Stage

Seed | Alive

About ODIN

ODIN is a cloud-based system for automating the management and operation of real estate, which allows users to debug the work of employees, reduce tenant outflows, increase cost transparency, and assess the accuracy of the state of objects.

ODIN Headquarter Location

st. Flotskaya, 5, Bldg. BUT

Moscow, 125413,

Russian Federation

+7 (495) 003 81 56

ODIN's Product Videos

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ODIN's Products & Differentiation

See ODIN's products and how their products differentiate from alternatives and competitors

  • 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.

    Differentiation

    ODIN is the only cloud CAFM/OEM fast deploying solution in CIS and EU. Similar service is provided by UpKeep in US. 

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    Differentiation

    We're on a mission to enable every organization to make smarter decisions about tech. Whether it's finding a new game-changing vendor or understanding a new market, it's easier, faster and smarter with CB Insights. All made possible by the smartest, hardest-working team in tech. Subscribe to see more.

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.

R

Real Estate Tech

2,258 items

Startups in the space cover the residential and commercial real estate space with a focus on consumers. Categories include buying, selling and investing in real estate (iBuyers, marketplaces, investment/crowdfunding platforms), and also tenant experience, property management, et

ODIN Patents

ODIN has filed 9 patents.

The 3 most popular patent topics include:

  • Automation
  • Building automation
  • Building engineering
patents chart

Application Date

Grant Date

Title

Related Topics

Status

8/22/2019

2/22/2022

Fishing equipment, Fishing techniques and methods, Recreational fishing, Fishing knots, Hitch knots

Grant

Application Date

8/22/2019

Grant Date

2/22/2022

Title

Related Topics

Fishing equipment, Fishing techniques and methods, Recreational fishing, Fishing knots, Hitch knots

Status

Grant

Latest ODIN News

NSWC Dahlgren develops decision aid for high-energy laser fire control

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 Web Traffic

Rank
Page Views per User (PVPU)
Page Views per Million (PVPM)
Reach per Million (RPM)
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ODIN Rank

  • 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 ODIN's competitors?

    Competitors of ODIN include HqO and 2 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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