Traditional AI implementations can take months just to prototype. Prototyping with Mantium takes minutes. Once you deploy and share your prototype, you’ll start getting the feedback needed to refine your models and quickly progress to the next stages of development.
It can be hard to explain what you’re trying to achieve with AI, so creating a prototype is a critical step in getting your point across, and the project off the ground. Share your prototypes with management to get quick, cross-functional buy-in.
Eliminate the disappointment and wasted time of delivering an undesired result that was created in a vacuum. Working collaboratively on an AI project speeds up the time to a finished product considerably. Transparency keeps the project moving steadily in the right direction.
Ensure the right message comes across in the output of your AI. The more people that run your prototypes during testing, the better your chances of ensuring your AI has the desired impact in production.
Personalizing your prototype will help your team with visualization. Use your organization’s branded colors or come up with your own creative blend. It takes just a few clicks to customize your apps.
Voting on prototypes provides valuable feedback that you can use to refine your AI. When collaborators like and dislike prototypes, Mantium stores the votes in the logs. You can see a consolidated view of all the liked output your prototype receives, as well as who liked it. This will help you understand if the model is performing well, even if you are not a subject matter expert yourself. You can then take the highly rated output and use it to fine tune the model.
Mantium captures the interactions with your AI so that you can audit its performance over time. Use the logs to understand how people interact with your AI, fine tune models, and maintain visibility for governance.
Sharing your AI during development is critical for gathering feedback so that you can gain insights and adjust settings. Get members of your organization using your prototypes and start collecting valuable feedback. The sooner you get the data needed to make improvements, the sooner you deploy your AI into production.
Once your team starts using your prototype, the inputs they create and the outputs of the model will all be captured in the logs. This will help you adjust settings and may reveal design errors, which you can then easily fix. The inputs provided by multiple people are likely to be very different, which will test the limits of your model.