Invision

Invision specializes in enhancing edge devices with AI capabilities, allowing these devices to recognize and track objects and detect anomalies. Their AI software is remarkably efficient, suitable for mainstream processors, and can be deployed on a large scale, from hundreds to hundreds of thousands of devices, both technically and financially.

The company has innovatively developed its machine-learning technology from the ground up. This development focuses on drastically reducing the memory and computational demands while achieving accuracy that rivals the best cloud systems. Known as AI Software Acceleration, this approach pairs well with the latest low-power, efficient hardware, delivering unmatched performance and value.

Invision Features

• Object and Movement Detection: Equipped to enable edge devices to recognize and track objects and movements, enhancing their environmental awareness.

• Object Classification: Features advanced object classification technology, allowing devices to accurately identify various objects.

• Facial Identification Technology: Incorporates facial recognition for precise identification and tracking.

• Persistent Object Tracking: Utilizes effective object-tracking methods for continuous monitoring and movement detection.

• Data Analysis Capabilities: Offers in-depth analytics tools, essential for processing and understanding data from edge devices.

• Unusual Event Detection: Specializes in identifying anomalies, improving the ability of devices to spot irregular or unexpected occurrences.

• Efficient AI Software Processing: Features an innovative approach to streamline complex neural networks, maintaining accuracy with minimal computational requirements. This technology is adaptable for both low-power IoT devices and higher-end applications.

• Diverse AI Framework Utilization: Combines various frameworks like TensorFlow and Torch for network training, complemented by a bespoke C++ software stack for enhanced performance and efficiency.

• Hardware Compatibility: The software is adaptable to a broad range of hardware, from basic ARM processors to sophisticated GPUs and FPGAs, ensuring flexibility and continual performance enhancements.

• Advanced Sensor Integration: Designed for multi-sensor integration in systems such as driver assistance technologies, processing asynchronous data from different sources for increased accuracy and reduced computation.

• Versatile AI Function Deployment: Facilitates the operation of AI functions across edge devices, cloud, or local fog servers, crucial for applications needing consistent performance irrespective of network connectivity

Invision Pricing

Custom pricing plan as per requirement.

Invision Usages

• Environmental Monitoring and Surveillance: Enhances surveillance systems by enabling edge devices to detect and track objects, people, and movements in various environments.

• Retail and Inventory Management: Utilizes object classification and tracking for efficient inventory management and retail analytics, aiding in stock monitoring and customer behavior analysis.

• Security and Access Control: Employs facial recognition technology for secure access control and identity verification in security-sensitive areas.

• Traffic and Crowd Management: Applies object and movement detection for managing traffic flow and crowd dynamics, useful in urban planning and event management.

• Industrial Automation and Safety: Offers anomaly detection for industrial environments, improving safety by identifying unusual events or malfunctions in machinery.

• Smart Home and IoT Applications: Incorporates data analysis capabilities in smart home devices, enhancing automation and user experience through intelligent decision-making.

• Automotive and Driver Assistance Systems: Utilizes sensor fusion technology for advanced driver assistance systems, enhancing vehicle safety and navigation.

• Network-Independent Operations: Ensures reliable operation of security, safety, and assistance systems even during network outages, by deploying AI functions on edge, cloud, or local fog servers.

Invision Competitors

• Tinyeinstein: A smaller-scale company with notable growth, focusing on AI and technology solutions. 

• Ada: A substantial competitor in the field with a large workforce and significant funding, showcasing its prominence in the industry. 

• Ideal: Known for its modest revenue and dedicated team, Ideal has been making strides in the market with considerable funding. 

• Neurovine: With moderate revenue and a growing team, Neurovine competes in the AI sector, despite some challenges in employee growth. 

• Tealbook: A significant player with a large team and considerable funding, Tealbook has established itself in the industry.

• DarwinAI: Garnering attention with its revenue and team size, DarwinAI has seen varied growth in the employee sector. 

• EnPowered: Competing with a notable revenue and a substantial team, EnPowered has been navigating the market despite some decreases in growth. 

• TimeHero: A smaller competitor, TimeHero operates with a lean team but has made its mark in the AI field.

Invision Launch & Funding

It was founded by Karim Ali in the year of 2016. Invision AI Raised a total $1,100,000 funding.

Invision Limitation

• Dependence on Environmental Conditions: The effectiveness of object detection and tracking may be limited in varying environmental conditions, affecting accuracy in surveillance or monitoring applications.

• Facial Recognition Challenges: Facial identification technology can face limitations due to factors like poor lighting, occlusions, or varying angles, potentially impacting security and access control systems.

• Hardware Dependency: While offering platform-agnostic solutions, the performance of Invision AI's software may vary depending on the hardware used, affecting its efficiency in different applications.

• Data Privacy Concerns: The use of facial recognition and data analytics raises privacy concerns, requiring stringent data handling and privacy policies, especially in sectors like retail and smart home applications.

• Complexity in Sensor Integration: The advanced sensor fusion technology, while beneficial, could be complex to implement and manage, especially in sophisticated systems like driver assistance technologies.

• Network Reliability for Cloud Services: Despite support for edge and fog computing, some functionalities relying on cloud services may be impacted during network disruptions, affecting industries like industrial automation.

• Customization and Integration Challenges: Tailoring AI solutions to specific use cases and integrating them with existing systems can be challenging, requiring technical expertise and potentially increasing implementation time.