Gyrus AI
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About
Gyrus AI is an advanced video analytics platform designed to modernize how organizations manage, search, and utilize their video assets. At its core, the platform leverages semantic media search technology, which allows users to find specific moments, objects, or individuals within a video library using natural language or visual cues rather than relying on manual metadata entry. This solution addresses the growing challenge of managing exponential increases in video content, providing a way to turn "dark" data into accessible, searchable information for various business applications. The technology stack incorporates sophisticated machine learning and computer vision algorithms to perform identity recognition and vision analytics. In practice, this means the system can automatically index footage, recognizing faces, logos, and specific actions. One of its standout features is its ability to integrate directly with existing Media Asset Management (MAM) and Digital Asset Management (DAM) systems. This integration transforms legacy repositories into intelligent, searchable databases, significantly reducing the time production teams spend searching for specific clips or segments within their archives. This platform is primarily tailored for high-volume media environments, such as broadcasters, content creators, and large enterprise organizations. For broadcasters specifically, it solves the problem of navigating decades of archived footage, making it possible to retrieve relevant historical clips in seconds for live news or documentary production. It is also beneficial for any industry requiring large-scale video monitoring or detailed visual data analysis, where human oversight alone is insufficient or too costly to maintain manually. What distinguishes Gyrus AI from standard video search tools is its emphasis on precision and environmental tailoring. Founded by a team of industry veterans from major semiconductor and technology firms like Qualcomm and Broadcom, the company focuses on delivering high-accuracy algorithms that are optimized for specific use cases. Furthermore, the platform is designed with enterprise-grade standards in mind, ensuring GDPR compliance and offering flexible deployment strategies to meet the security and infrastructure needs of large-scale operations.
Pros & Cons
Seamless integration with existing MAM systems like Media Hive for easier workflow transitions.
Founded by industry veterans with 20+ years of experience at top-tier firms like Qualcomm and Broadcom.
Algorithms are tailored to specific environments to ensure high precision and recall for every use case.
Global presence with technical operations and support across the USA, UK, Europe, Brazil, and Japan.
Pricing is not transparently listed and requires a custom demo request for any quote.
Detailed minimum system requirements are not publicly available without contacting their technical team.
Use Cases
Broadcasters can transform legacy MAM systems into smart searchable archives to retrieve specific clips instantly for live news.
Media organizations can automate the tagging and indexing of large video libraries to reduce manual labor and improve discoverability.
Enterprise security teams can utilize identity recognition and vision analytics to monitor video feeds for specific events or individuals.
Data scientists can leverage the platform's vision analytics to generate predictive insights from large-scale visual datasets.
Platform
Features
• enterprise-grade security
• predictive insights
• contextual search
• vision analytics
• mam and dam integration
• automated media indexing
• identity recognition
• semantic media search
FAQs
Can Gyrus AI integrate with my existing Media Asset Management (MAM) system?
Yes, Gyrus AI is designed to integrate with existing DAM and MAM systems, such as Tecla Systems’ Media Hive. This allows organizations to add semantic search and automated indexing capabilities to their current workflows without needing to replace their entire infrastructure.
Is the platform compliant with international data regulations like GDPR?
Yes, Gyrus AI is GDPR compliant, ensuring that video processing and data handling meet strict privacy standards. This is particularly important for organizations using identity recognition features in regions with rigorous data protection laws.
What industries benefit most from using Gyrus AI?
While versatile, the tool is primarily built for broadcasters and media organizations managing vast video archives. It is also highly effective for enterprise-level video analytics, where identity recognition and automated vision processing are required for productivity.
How do I get started with a deployment?
Potential users can request a demo through the official website to see the solutions in action. The company provides expert consultations to discuss specific deployment options and system requirements tailored to the organization's needs.
Pricing Plans
Enterprise
Unknown Price• Semantic Media Search
• Identity Recognition
• Vision Analytics
• MAM/DAM Integration
• Automated Media Indexing
• GDPR Compliance
• Dedicated Technical Support
• Custom AI Algorithm Tailoring
Job Opportunities
Full Stack Engineer. AWS Expertise
Streamline video workflows and automate media indexing with semantic search and identity recognition, enabling broadcasters to find specific content in seconds.
Education Requirements:
Bachelor's, Master's in Computer Science or equivalent experience
Experience Requirements:
2+ Years of job-related experience
Other Requirements:
Proficiency within C++ or Python
Basic DevOps skills
Good experience in MongoDB, Cassandra, Kafka
Broad experience in overall system design and implementation
Ability to design scalable systems
An understanding of domain modeling and fluent interface
Experience applying design patterns in concurrency scenarios
Familiarity with Web technologies (HTML, CSS, DOM, JavaScript, and SVG)
Proven experience in designing persistence and caching models using both SQL and NoSQL
Responsibilities:
Design, build, and maintain data infrastructure for both production and internal research
Design platform and tools to facilitate research
Administer and maintain computing cluster
Maintain production job scheduler
Setup and Maintain authentication systems for API's
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Signal Processing Engineer
Streamline video workflows and automate media indexing with semantic search and identity recognition, enabling broadcasters to find specific content in seconds.
Education Requirements:
MS/BS in EE or CS
Experience Requirements:
5+ yrs developer experience required
Required MatLab/Octave: 5 years
Other Requirements:
Extensive C or C++ programming experience in real-time embedded systems required
MatLab or GNU Octave DSP Algorithm development and simulation experience
In-depth knowledge and development experience with wireless communication protocols
Strong analysis, design and debug skills
Ability to work independently, meet project deadlines and document work
Responsibilities:
Design, Analyze and Develop Wireless systems
Design wireless algorithms for wireless sensing, denoising for various real-world scenarios.
Test and optimize the algorithms to meet real-time and performance requirements.
Integrate sub-systems and test functionality at the system level along with other ML/AI algorithms.
Measure and optimize to meet system-level performance and operational requirements
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AI or Machine Learning Engineer Bangalore
Streamline video workflows and automate media indexing with semantic search and identity recognition, enabling broadcasters to find specific content in seconds.
Education Requirements:
Bachelors Degree in Computer Science or related field required
Experience Requirements:
Minimum 2 years experience designing and implementing time series forecasting
Minimum 2 years experience designing and implementing machine learning solutions
Other Requirements:
Object oriented programming skills in one of Scala, Java, Python, C++
Strong knowledge and understanding of machine learning pipelines
Common data science toolkits, such as NumPy Pandas scikit-learn Matplotlib etc
Deep learning frameworks such as PyTorch TensorFlow
SQL and NoSQL databases such as PostgreSQL Cassandra MongoDB etc.
Responsibilities:
Design machine learning systems
Research and implement appropriate ML algorithms and tools
Develop machine learning applications according to requirements
Select appropriate datasets and data representation methods
Train ML/AI algorithms and test them for accuracy, precision and recall
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Ratings & Reviews
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