Revolutionizing Video Editing: A Look into VideoVerse's Innovative Platform

Video editing is significantly sped up in the sense that what once took an hour or more is now done by Magnifi in only 35 to 45 seconds.

Ashok Pandey
New Update
Audio & Video Editing Software

In the rapidly evolving world of video editing, VideoVerse has emerged as a game-changer. With their flagship product, Magnifi, VideoVerse leverages cutting-edge AI and machine learning technologies to deliver seamless video editing experiences to users. We spoke to Ashwin, CPO of VideoVerse, to dive deep into the technical architecture of VideoVerse's platform, explore their innovative use of AI and cloud computing, examine their commitment to security and ethics, and discuss the integration of emerging technologies like virtual reality and augmented reality.


A deep dive into the platform's underlying technical structure

Our flagship product, Magnifi, uses several custom AI models. Magnifi’s custom AI model leverages an active, self-learning workflow, which is ideal for when data is available in abundance, but it is often unlabeled: We have invested in techniques for our AI models to learn actively from the data and be able to quickly add new labels to capture nuances in the various sports that we analyse.

A system of AI models will read and analyse uploaded video content, auto-generate clips based on specific meta-tags, execute quality control of the audio and video, and deliver the curated content to the user. Video editing is significantly sped up in the sense that what once took a human editor up to an hour or more is now done by Magnifi in only thirty-five to forty-five seconds.

The cloud-based approach


As a startup that specializes in analysing sporting game streams using computer vision machine learning, our technology relies on cloud-based infrastructure to process large amounts of streaming data in real-time.

One important aspect of our proprietary technology is the use of machine learning algorithms to identify and track key events in the sporting game streams. This involves analysing video frames to identify players, the ball, and other relevant objects, as well as tracking their movements over time.

To handle the real-time processing of this streaming data, our technology utilizes cloud-based servers that are capable of performing the necessary computations quickly and efficiently. We also employ techniques such as data parallelism and distributed computing to further optimize our processing capabilities.


In addition, our technology may use advanced compression techniques to reduce the size of the streaming data and content delivery networks (CDNs) to distribute the data processing workload across multiple servers and locations. Both of these reduce latency and improve the overall processing speed, which is in keeping with our commitment to delivering high-quality, fast, and reliable sports game analysis to our clients.

Harnessing AI and Machine Learning in Video Content Tagging

Once Magnifi’s model is trained on a particular domain, such as a sport, it can automatically identify key moments through the use of a meta-tag. A meta-tag in soccer is “goal” for example. Using that meta-tag will call all the goals from a particular game. Users could refine this filter by adding a specific athlete, such as Cristiano Ronaldo, which will auto-generate a clip of all the goals scored by him. This auto-generation based on meta-tags spares video editors of manually sifting through hours and hours of footage to find just what they are looking for.


Magnifi’s AI model also enhances downstream editing tasks, such as auto-flipping, which automatically resizes videos to the ideal dimensions of a channel; personalization, which enables organizations to imprint their visual identity with the push of a button; and auto-publishing, which helps them distribute the video to up to thirty different channels.

VideoVerse’s other product, illusto, helps users transform raw footage into compelling stories through the use of effects, transitions, filters, chromatic correction, voiceovers, text overlays, and other key details.

Security Challenges and Safeguards at VideoVerse


VideoVerse understands the critical importance of ensuring the security of its platform and protecting user data from potential threats such as hacking or malware attacks.

To achieve this goal, VideoVerse uses a variety of best-in-class security techniques. Encryption is used to protect user data both at rest and in transit, making it unreadable and unusable to anyone who does not have the correct decryption key. Authentication and access control measures ensure that only authorized personnel can access the platform and user data, while regular security audits help identify and address any potential weaknesses in the system. Disaster recovery plans provide a roadmap for restoring the platform and data in the event of a catastrophic incident. Finally, employee training on best practices in cybersecurity ensures that all personnel understand the importance of security and how to prevent potential threats.

VideoVerse also closely monitors its platform for any suspicious activity or potential threats. This involves real-time monitoring of network traffic, system logs, and user behavior to detect and respond to potential security incidents quickly. While no security measure can provide 100% protection, VideoVerse is committed to doing everything it can to ensure the security and integrity of its platform and the protection of user data.


Addressing Ethical Concerns in AI and Machine Learning at VideoVerse

VideoVerse is aware of the ethical concerns surrounding AI-powered technologies, such as the possibility of perpetuating biases or making unjust decisions. While ethical considerations are not a current issue for VideoVerse, the company is committed to staying up-to-date on evolving regulations and technology to ensure that its algorithms are fair and unbiased.

For example, VideoVerse is actively researching explainable AI to enable users to understand how its algorithms are making decisions and to identify potential biases. By seeking to provide explanations for how the algorithm arrived at a given decision, VideoVerse aims to reduce the potential for biased outcomes. This initiative is in keeping with VideoVerse’s overall approach of proactively addressing these concerns while maintaining user and customer privacy.