As the models behind the tools become more sophisticated, the use of artificial intelligence in content creation is expected to increase. Ken Wang, founder and CEO of BOOLV, was at the BEYOND expo in mid- May. BOOLV is a Hong Kong-based startup that makes artificial intelligence-powered tools to speed up content creation in marketing.
Baby clothing retailer Patpat, fast fashion platform Cider, and clothing site Cozinen are some of the brands served by BOOLV. Wang and his team are skilled in data handling, artificial intelligence, and product management.
Video Maker is an artificial intelligence tool that helps retailers make marketing clips, and Booltool is a marketing content platform that offers creation tools for copies, pictures, and videos.
Most of the commercial videos will be generated using machine assistance. High video production costs will be a challenge as video becomes the most common form of content. It is important to have a productivity tool to address this problem. In order to assist global small and medium-sized enterprises in generating high- value videos in the most efficient manner possible, our company is founded.
The interview was conducted in Chinese and was edited for clarity.
Short videos for e- commerce are understood by us. We use our own models to handle video clips in our product. It doesn't have anything to do with large models.
To get the best return on investment, we help brands create unique videos. We analyzed many video script to determine what tone suits specific advertising objectives and provided suggestions as functions. Transition effects and virtual characters are some of the factors considered to achieve the desired impact. A lot of the best-performing video formats have been developed by us.
These offerings are not solely technical, they are more about deconstructing videos and refining best practices. Sharing this knowledge with global users creates value. This is what makes us competitive.
We don't pay attention to that. The results could be similar. Our performance is expected to improve because of the training we have done.
We use some of the features directly, without having to change anything. We understand our users' pain points and needs and try to find application oriented solutions. Users care more about the end result when we stack multiple models, train ourselves, or use large-scale models.
When creating content for e- commerce, our product addresses issues throughout the user's workflows. Some users could use a generalized model, but it wouldn't work for everyone. Competitive pricing for package solutions is offered by us.
We are focused on specific needs in e- commerce, so we are not worried about that.
Adobe wouldn't invest in face- swapping because it's not generalized demand. Face swaps are often used in e- commerce scenarios. I need models that show the same outfit. I could replace one model's face with that of a person from a different ethnic group. Users in other sectors can't use this.
Larger companies probably won't be as good as what we offer if they incorporate such features. We have trained our models to work for e- commerce, so we are not concerned about that.
