Flixier Pro is an advanced online video editing tool. The key components of the product are amazingly fast rendering time that doesn’t require a high end computer, Google Docs collaboration style for video editors and all the actors involved in the process (producers, managers, etc.), streamlined communication for draft reviews, and easy access to plenty of cloud storage.

The main advantages of Flixier are:

Fast Rendering - Flixier Pro can render any video that is up to 60 minutes long in under one minute. It also does rendering in the cloud freeing up your computer for other tasks. The great thing about this is that you don’t need to spend a ton of money on expensive hardware.

Streamlined Collaboration - Getting feedback with Flixier Pro is as easy as clicking a button and sharing a link. People can start adding comments that show up directly in the video editing software. This is amazing because now you only need to render when the video is complete. And rendering is blazing fast, read above. Flixier Pro users can easily share their assets with colleagues. This is great because until now it was very frustrating to share libraries and projects and keep them in sync. Even more, team members can work together in real time on the same project, just like in Google Docs

Cloud Storage - We remove the need of having to buy expensive storage drives, worry about carrying them around or having to upgrade your computer, just to add a few gigabytes of storage.

Labelf AI aims to let anyone, no matter previous knowledge, create and use AI text classification models. We use an advanced approach to active learning to accelerate training on both labeled and unlabeled data. Can also be used for data analysis and will hook into most Customer support systems. Can be used for Document Classification, Automated Product Tagging, Automatic Support Ticket Tagging, Chat and Forum Moderation, Social Listening, Survey Analysis, Sales Stage Identification, Finding Sales leads and much more. Labelf models can also classify texts in any language despite having only been trained on data in one language.