Post-houses deal with vast amounts of footage, leading to duplication, unnecessary storage costs, a higher carbon footprint and hundreds of person-hours lost finding the right asset.
Finding the right scene or segment in your content archive, for editing or creating showreels, is heavily time-consuming without tag data. AI tag data is expensive to generate and manual tagging is labour-intensive and subjective.
Match – deduplicates video content, reducing storage needs by an average of 60%, resulting in the same reduction of cost and carbon emissions. With fewer assets to check, finding the right one becomes a lot easier.
Match tags your content at ~25% of the normal AI cost and carbon footprint by finding all unique images and audio across your assets, ensuring you never pay to tag the same thing twice. It also checks every frame and millisecond of audio, so nothing gets overlooked. The data is returned to your DAM/MAM/Editor empowering your searches in the tool you normally use.
Harmonise – builds relationships between every millisecond of video, image and audio across all of your assets. This enables you to see where every millisecond came from and where else it is used. For example, if you find an issue in one of your assets, Harmonise can easily show you everywhere else it appears, ensuring nothing is missed and saving you hundreds of person-hours.
Compose – automates the creation of IMF packages from traditional versions, including CPL generation and metadata integration. This enables you to quickly create delivery-ready files to destinations like Netflix, which accepts IMF, directly from your existing archive of traditional content.
If you use IMF as your archive format, your archive size can be reduced by up to 90%. This reduces the cost and carbon emissions of storage by up to 90%.
