Collaboration - Mantium

85%

AI and machine learning projects fail to deliver

53%

Project that make it from prototypes to production

Fast Prototyping

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.

Get management buy in

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.

Deliver the right product

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.

Guarantee impact

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.

Use custom styling

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.

Prototype voting

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.

There is a Goldmine in the Logs

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.

Deploy, share, and gather feedback

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.

Gain insights from logs

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.

See how monitoring and logs can give you unique insights into performance before it becomes an issue.

Learn more

Bring in the team

To create an effective AI, knowledge experts need to bring their wisdom to the table.
Multiple eyes on a project helps mistakes get spotted quickly so that development can continue.
Sometimes, generating ideas is the hardest part. Crowdsource ideas with your team.
Collaboration improves the quality of output by increasing the diversity of input.
Keep focus on the problem. Distraction is less likely with more people on the project.
Get the right data from the right people. Let the team contribute their data sources.

Collaboration drives AI projects to deployment faster.