Spatiotemporal Multimodal Data Platform

Data-driven development, testing, production, and operation of robotic companies

Spatiotemporal Multimodal Data Platform

Data-driven development, testing, production, and operation of robotic companies

One-stop digitalization,

End-to-end machine data management

One-stop digitalization,

End-to-end machine data management

coScene's platform features exceptional capabilities for automatic data collection, real-time monitoring, and diagnostic analysis. Our platform ensures the processes of data cleansing, labeling, and evaluation, allowing you to fully utilize multimodal data efficiently through semantic search, automated workflows, and comprehensive visual interfaces.

  • Real-time monitoring devices, online data collection

  • Chart analysis

  • Maps and route planning

  • Visualize data online, freely set laser point clouds

  • Real-time monitoring devices, online data collection

  • Chart analysis

  • Maps and route planning

  • Visualize data online, freely set laser point clouds

  • Real-time monitoring devices, online data collection

  • Chart analysis

  • Maps and route planning

  • Visualize data online, freely set laser point clouds

AI Smart Era,Data challenges urgently need to be solved

Non-structural spatio-temporal data explosion

High difficulty in handling and high threshold

Pain Point 1

Spending 80% of the time on data cleaning and preprocessing

Unable to focus on model development and optimization

Pain Point 2

Endless long-tail use cases

Complex equipment and data are difficult to manage

Pain Point 3

Team data processing toolchain mismatch

Lack of effective data sharing and collaboration mechanisms

Pain Point 4

Data quality issues lead to unstable models

The workload of debugging and optimization is enormous

Pain Point 5

Build the data processing flow from scratch

costs a lot of manpower and time

Pain Point 6

Non-structural spatiotemporal data explosion

High processing difficulty and high threshold

Pain point 1

Spend 80% of the time on data preprocessing

Unable to focus on model development and optimization

Pain Point 2

Endless use cases

Complex devices and data are difficult to manage

Pain Point 3

Unmatched data processing toolchains between teams

Lack of effective data sharing and collaboration mechanisms

Pain Point 4

Data quality issues lead to unstable models

The workload of debugging and optimization is huge

Pain Point 5

Build the data processing workflow from scratch

Spends a lot of manpower and time

Pain Point 6

coScene Intelligent Engine : Closing the Data Loop Across the Entire Chain

Scenario-based Data Operation and Maintenance

Scene-based Workflow Engine

Semantic Search and Data Discovery

Scenario-based Data Operation and Maintenance

Scene-based Workflow Engine

Semantic Search and Data Discovery

Data Platform

Data Governance and Discovery

Quality and Safety Assurance

Enhance Collaboration

Player

Spatiotemporal Data Visualization

Web Development Debugging Tool

Edge Agent

Remote Access

Intelligent Data Collection

Testing platform

Large-scale parallel testing

Simulation Integration and Evaluation

Three Core Competencies

Diagnosis

Feedback

Collection

Deployment

Research and Development

Powerful features, covering multiple aspects of production and research

Research and Development

Algorithm Optimization, Parameter Tuning, Visual Analysis, Ground Truth Annotation

Testing

Regression Testing, Simulation Verification, Robustness Testing, Compliance Review

Production

Offline Inspection, Device Labeling, Environmental Scanning, Task Deployment

Operation and Maintenance

Alarm classification, automatic diagnosis, issue tracking, remote operation and maintenance

Widely used, easily cope with various scenarios

1000GB

Cloud Testing

1000GB

Cloud Testing

Cloud Testing

1000 units

Scene Collection

1000 locations

Run Workflow

5000HRS

1000 Units

Machine Maintenance

1000 Places

Scene Collection

5000HRS

Run Workflow

Note: Data sourced from coScene's compilation

Win-win cooperation,Helping various industries solve data challenges

The trusted choice of many leading robot companies

The trusted choice of many leading robotics companies

  • "Overseas onsite support costs at least $300 each time. Remote data transmission, diagnosis, and issue resolution can greatly help us save operation and maintenance costs!"

    Gaussian Robotics Chief Systems Engineer

  • Engineers can close the loop faster; know the quality of each version every day, and achieve rapid iteration, rapid rollback, and quickly understand where the problem is

    Evolution Director of Algorithms

  • "Originally, only 5 machines’ scheduling could be tested, using Krone can test 20 machines instead."

    Gaussian Robotics

  • "Simulation testing can cover 60% of our field testing, greatly saving the cost of manual testing."

    KEENON Robot Algorithm Director

  • Previously, it took 2 days to run a full-scale test with 400 pieces of data, but now it can be completed in just 2 hours!

    KEENON Robot Algorithm Director

  • The user interface is simple to operate, no need to install a virtual machine, much simpler to train overseas operations and maintenance teams.

    Gaussian Robotics Hotel Operations Manager

  • "No need to download and replay repeatedly anymore! My team of 6-7 people can save 80 hours per week."

    Gaussian Robotics Head of PAE

  • "No need to download and replay repeatedly anymore! My team of 6-7 people can save 80 hours per week."

    Gaussian Robotics Head of PAE

  • "Overseas onsite support costs at least $300 each time. Remote data transmission, diagnosis, and issue resolution can greatly help us save operation and maintenance costs!"

    Gaussian Robotics Chief Systems Engineer

  • The user interface is simple to operate, no need to install a virtual machine, much simpler to train overseas operations and maintenance teams.

    Gaussian Robotics Hotel Operations Manager

  • "Simulation testing can cover 60% of our field testing, greatly saving the cost of manual testing."

    KEENON Robot Algorithm Director

  • "Originally, only 5 machines’ scheduling could be tested, using Krone can test 20 machines instead."

    Gaussian Robotics

  • Previously, it took 2 days to run a full-scale test with 400 pieces of data, but now it can be completed in just 2 hours!

    KEENON Robot Algorithm Director

  • Engineers can close the loop faster; know the quality of each version every day, and achieve rapid iteration, rapid rollback, and quickly understand where the problem is

    Evolution Director of Algorithms

Our partners are distributed globally

Frequently Asked Questions

What aspects does the data closed loop include?

coScene proposes the concept of spatiotemporal data containerization and has independently developed three major engines: the Data Container Engine, Workflow Engine, and Semantic Search Engine. These engines greatly enhance the capabilities of accessing, querying, governing, and scheduling data computation for unstructured data, becoming the cornerstone of various data applications. Currently, the company has implemented four major functional modules: the Control Console, Data Platform, Visualization Player, and Testing Platform, providing users with a service loop for the research and development process.

What aspects does the data closed loop include?

coScene proposes the concept of spatiotemporal data containerization and has independently developed three major engines: the Data Container Engine, Workflow Engine, and Semantic Search Engine. These engines greatly enhance the capabilities of accessing, querying, governing, and scheduling data computation for unstructured data, becoming the cornerstone of various data applications. Currently, the company has implemented four major functional modules: the Control Console, Data Platform, Visualization Player, and Testing Platform, providing users with a service loop for the research and development process.

How can coScene empower enterprises?

With the rapid development of artificial intelligence, the demand for training data is constantly increasing. The AI industry is transitioning from single structured data to multi-modal and spatiotemporal data. Additionally, the robotics industry is also experiencing rapid growth. However, the rapid development of robots can easily lead to high-cost traps. coScene will realize enterprise data-driven operations, bringing a 700% increase in productivity, and ultimately become the core competitive advantage of robotics companies.

How can coScene empower enterprises?

With the rapid development of artificial intelligence, the demand for training data is constantly increasing. The AI industry is transitioning from single structured data to multi-modal and spatiotemporal data. Additionally, the robotics industry is also experiencing rapid growth. However, the rapid development of robots can easily lead to high-cost traps. coScene will realize enterprise data-driven operations, bringing a 700% increase in productivity, and ultimately become the core competitive advantage of robotics companies.

What are the application scenarios of coScene?

The application scenarios for coScene are extremely diverse, including but not limited to: smart home, industrial assembly, cafe management, and warehouse logistics.

What are the application scenarios of coScene?

The application scenarios for coScene are extremely diverse, including but not limited to: smart home, industrial assembly, cafe management, and warehouse logistics.

How can I use the coScene platform?

Currently, coScene supports multiple interaction methods, including web platforms and various integrated features, allowing you to easily deploy and use it.

How can I use the coScene platform?

Currently, coScene supports multiple interaction methods, including web platforms and various integrated features, allowing you to easily deploy and use it.

What is the development history of coScene?

2022: In May, coScene was established and received tens of millions of angel round financing in August. In October, we developed a multimodal data engine, provided diagnostic and collaborative capabilities, and successfully delivered the MVP, signing a seed customer agreement. 2023: In February, we joined AliCloud Computing Nest and signed cooperation agreements with Shanghai Software Center and others. In August, we successfully delivered a complete testing platform, and the cluster management experience completed the first-stage closed loop. We gained recognition from more than three top-tier paid robot head enterprises, including GAUSSIAN, KEENON and EVOLUTION. In November, we visited Figure.ai and began technical requirement docking. In December, we signed with Supor, officially entering the field of smart home appliances. 2024: In January, we integrated annotation platforms, simulation platforms, and other ecosystems, and signed with AGIBOT. In June, we vigorously promoted the research and development closed loop of physical robots, and cooperated with SHANGHAI ROBOT INDUSTRIAL TECHNOLOGY RESEARCH CENTER, Shanghai's top three universities, and launched data standardization services. We also began to expand into overseas markets. In September, we developed annotation integration and semantic search engines, completed PMF, and carried out extensive cooperation and integration. We also provided a marketplace and established an ecosystem.

What is the development history of coScene?

2022: In May, coScene was established and received tens of millions of angel round financing in August. In October, we developed a multimodal data engine, provided diagnostic and collaborative capabilities, and successfully delivered the MVP, signing a seed customer agreement. 2023: In February, we joined AliCloud Computing Nest and signed cooperation agreements with Shanghai Software Center and others. In August, we successfully delivered a complete testing platform, and the cluster management experience completed the first-stage closed loop. We gained recognition from more than three top-tier paid robot head enterprises, including GAUSSIAN, KEENON and EVOLUTION. In November, we visited Figure.ai and began technical requirement docking. In December, we signed with Supor, officially entering the field of smart home appliances. 2024: In January, we integrated annotation platforms, simulation platforms, and other ecosystems, and signed with AGIBOT. In June, we vigorously promoted the research and development closed loop of physical robots, and cooperated with SHANGHAI ROBOT INDUSTRIAL TECHNOLOGY RESEARCH CENTER, Shanghai's top three universities, and launched data standardization services. We also began to expand into overseas markets. In September, we developed annotation integration and semantic search engines, completed PMF, and carried out extensive cooperation and integration. We also provided a marketplace and established an ecosystem.

Contact us

AI Intelligent Era, Data challenges await urgent solutions

Non-structural spatiotemporal data explosion

High processing difficulty and high threshold

Pain Point 1

Spend 80% of the time on data preprocessing

Unable to focus on model development and optimization

Pain Point 2

Endless use cases

Complex devices and data are difficult to manage

Pain Point 3

Pain Point 6

Build the data processing flow from scratch

takes a lot of manpower and time

Pain Point 5

Team data processing toolchains do not match

Lack of effective data sharing and collaboration mechanisms

Pain Point 4

Data quality issues lead to unstable models

Significant amount of work for debugging and optimization

AI Intelligent Era, Data challenges await urgent solutions

Non-structural spatiotemporal data explosion

High processing difficulty and high threshold

Pain Point 1

Spend 80% of the time on data preprocessing

Unable to focus on model development and optimization

Pain Point 2

Endless use cases

Complex devices and data are difficult to manage

Pain Point 3

Pain Point 6

Build the data processing flow from scratch

takes a lot of manpower and time

Pain Point 5

Team data processing toolchains do not match

Lack of effective data sharing and collaboration mechanisms

Pain Point 4

Data quality issues lead to unstable models

Significant amount of work for debugging and optimization

coScene Intelligent Engine: Fully Connecting the Data Closed Loop

Data Platform

Data Governance and Discovery

Quality and Safety Assurance

Enhance Collaboration

Edge Agent

Remote Access

Intelligent Data Collection

Testing platform

Large-scale parallel testing

Simulation Integration and Evaluation

Three Core Competencies

Diagnosis

Feedback

Collection

Deployment

Research and Development

Player

Spatiotemporal Data Visualization

Web Development Debugging Tool

Scenario-based Data Operation and Maintenance

Scene-based Workflow Engine

Semantic Search and Data Discovery

coScene Intelligent Engine: Fully Connecting the Data Closed Loop

Data Platform

Data Governance and Discovery

Quality and Safety Assurance

Enhance Collaboration

Edge Agent

Remote Access

Intelligent Data Collection

Testing platform

Large-scale parallel testing

Simulation Integration and Evaluation

Three Core Competencies

Diagnosis

Feedback

Collection

Deployment

Research and Development

Player

Spatiotemporal Data Visualization

Web Development Debugging Tool

Scenario-based Data Operation and Maintenance

Scene-based Workflow Engine

Semantic Search and Data Discovery

Widely used, Easily adapt to various scenarios

1000GB

Cloud Testing

1000

Machine Maintenance

1000

Scene Collection

5000HRS

Run Workflow

Note: Data sourced from coScene's compilation

Widely used, Easily adapt to various scenarios

1000GB

Cloud Testing

1000

Machine Maintenance

1000

Scene Collection

5000HRS

Run Workflow

Note: Data sourced from coScene's compilation

Research and Development

Algorithm Optimization, Parameter Tuning, Visual Analysis, Ground Truth Annotation

Testing

Regression Testing, Simulation Verification, Robustness Testing, Compliance Review






Production

Offline Inspection, Device Labeling, Environmental Scanning, Task Deployment

Operations and Maintenance

Alarm classification, automatic diagnosis, issue tracking, remote operation and maintenance

Powerful Features, Comprehensive Coverage of Multiple R&D Stages

Our partners are distributed globally

The trusted choice of many leading robotics companies

  • "Overseas onsite support costs at least $300 each time. Remote data transmission, diagnosis, and issue resolution can greatly help us save operation and maintenance costs!"

    Gaussian Robotics Chief Systems Engineer

  • Engineers can close the loop faster; know the quality of each version every day, and achieve rapid iteration, rapid rollback, and quickly understand where the problem is

    Evolution Director of Algorithms

  • "Originally, only 5 machines’ scheduling could be tested, using Krone can test 20 machines instead."

    Gaussian Robotics

  • "Simulation testing can cover 60% of our field testing, greatly saving the cost of manual testing."

    KEENON Robot Algorithm Director

  • Previously, it took 2 days to run a full-scale test with 400 pieces of data, but now it can be completed in just 2 hours!

    KEENON Robot Algorithm Director

  • The user interface is simple to operate, no need to install a virtual machine, much simpler to train overseas operations and maintenance teams.

    Gaussian Robotics Hotel Operations Manager

  • "No need to download and replay repeatedly anymore! My team of 6-7 people can save 80 hours per week."

    Gaussian Robotics Head of PAE

Frequently Asked Questions

What aspects does the data closed loop include?

coScene has proposed the concept of spatiotemporal data containerization and independently developed three major engines: the Data Container Engine, Workflow Engine, and Semantic Search Engine. These three engines greatly enhance the capabilities of accessing, querying, governing, and scheduling data computation for unstructured data, becoming the cornerstone of various data applications. Currently, the company has implemented four major functional modules: the Control Console, Data Platform, Visualization Player, and Testing Platform, providing users with a service loop for the research and development process.

What aspects does the data closed loop include?

coScene has proposed the concept of spatiotemporal data containerization and independently developed three major engines: the Data Container Engine, Workflow Engine, and Semantic Search Engine. These three engines greatly enhance the capabilities of accessing, querying, governing, and scheduling data computation for unstructured data, becoming the cornerstone of various data applications. Currently, the company has implemented four major functional modules: the Control Console, Data Platform, Visualization Player, and Testing Platform, providing users with a service loop for the research and development process.

How can coScene empower enterprises?

With the rapid advancement of artificial intelligence, the demand for training data is continuously increasing. The AI industry is transitioning from single structured data towards multi-modal and spatiotemporal data. Additionally, the robotics industry is experiencing rapid growth. However, the rapid development of robots can easily lead to high-cost traps. coScene will realize enterprise data-driven operations, bringing a 700% increase in workforce efficiency, and ultimately become the core competitive advantage of robotics companies.

How can coScene empower enterprises?

With the rapid advancement of artificial intelligence, the demand for training data is continuously increasing. The AI industry is transitioning from single structured data towards multi-modal and spatiotemporal data. Additionally, the robotics industry is experiencing rapid growth. However, the rapid development of robots can easily lead to high-cost traps. coScene will realize enterprise data-driven operations, bringing a 700% increase in workforce efficiency, and ultimately become the core competitive advantage of robotics companies.

What are the application scenarios of coScene?

The application scenarios for coScene are extremely diverse, including but not limited to: smart home, industrial assembly, cafe management, and warehouse logistics.

What are the application scenarios of coScene?

The application scenarios for coScene are extremely diverse, including but not limited to: smart home, industrial assembly, cafe management, and warehouse logistics.

How can I use the coScene platform?

Currently, coScene supports multiple interaction methods, including web platforms and various integrated features, allowing you to easily deploy and use it.

How can I use the coScene platform?

Currently, coScene supports multiple interaction methods, including web platforms and various integrated features, allowing you to easily deploy and use it.

What is the development history of coScene?

2022: In May, coScene was established and received tens of millions of angel round financing in August. In October, we developed a multimodal data engine, provided diagnostic and collaborative capabilities, and successfully delivered the MVP, signing a seed customer agreement. 2023: In February, we joined AliCloud Computing Nest and signed cooperation agreements with Shanghai Software Center and others. In August, we successfully delivered a complete testing platform, and the cluster management experience completed the first-stage closed loop. We gained recognition from more than three top-tier paid robot head enterprises, including GAUSSIAN, KEENON and EVOLUTION. In November, we visited Figure.ai and began technical requirement docking. In December, we signed with Supor, officially entering the field of smart home appliances. 2024: In January, we integrated annotation platforms, simulation platforms, and other ecosystems, and signed with AGIBOT. In June, we vigorously promoted the research and development closed loop of physical robots, and cooperated with SHANGHAI ROBOT INDUSTRIAL TECHNOLOGY RESEARCH CENTER, Shanghai's top three universities, and launched data standardization services. We also began to expand into overseas markets. In September, we developed annotation integration and semantic search engines, completed PMF, and carried out extensive cooperation and integration. We also provided a marketplace and established an ecosystem.

What is the development history of coScene?

2022: In May, coScene was established and received tens of millions of angel round financing in August. In October, we developed a multimodal data engine, provided diagnostic and collaborative capabilities, and successfully delivered the MVP, signing a seed customer agreement. 2023: In February, we joined AliCloud Computing Nest and signed cooperation agreements with Shanghai Software Center and others. In August, we successfully delivered a complete testing platform, and the cluster management experience completed the first-stage closed loop. We gained recognition from more than three top-tier paid robot head enterprises, including GAUSSIAN, KEENON and EVOLUTION. In November, we visited Figure.ai and began technical requirement docking. In December, we signed with Supor, officially entering the field of smart home appliances. 2024: In January, we integrated annotation platforms, simulation platforms, and other ecosystems, and signed with AGIBOT. In June, we vigorously promoted the research and development closed loop of physical robots, and cooperated with SHANGHAI ROBOT INDUSTRIAL TECHNOLOGY RESEARCH CENTER, Shanghai's top three universities, and launched data standardization services. We also began to expand into overseas markets. In September, we developed annotation integration and semantic search engines, completed PMF, and carried out extensive cooperation and integration. We also provided a marketplace and established an ecosystem.

Win-win cooperation, Helping various industries solve data challenges

Start Now

Unlock the potential of data and feel the power of cloud computing

Ready to use out of the box

Start Now

Unlock the potential of data and feel the power of cloud computing

Ready to use out of the box

Start Now

Unlock the potential of data, feel the power of cloud computing

Ready to use out of the box

Start now

Unlock data potential Feel the power of cloud computing

Ready to use out of the box

Start now

Unlock data potential Feel the power of cloud computing

Ready to use out of the box