Overview
Zabble's is an early-stage start-up that tackles waste in commercial environments (mainly college campuses) and is looking to expand into healthcare and hospitality. They are betting on a new feature set described below to gain traction in these areas. 
AI for WASTE AUDITING

Zabble's iOS app already had a feature to snap a photo of a waste bin, but was now in the process of a major enhancement - to provide AI-based fullness and contamination based on an image. This technology was made possible by extensive research and development on neural networks and AI training of photography of waste. Adding this feature would save massive amounts of time for waste auditors in the field.

Here's where the original UI stood before I jumped into the project:
OPTIMIZING THE USER EXPERIENCE

I wanted to provide the user with a much more intuitive and simple way to not only take a photo, but also organize their entries all together. 

Knowing that the new AI-based image suggestions can save users time in the field, I wanted the access to the camera to be right at hand. Also, the entry could now be pre-filled, so the flow order needed a rethink as well. 


The updated entry flow:
PROTOTYPING for understanding

Piecing the full capacity of the new feature together helped us work out the kinks and answer questions before handoff to dev. I crafted the flow of capturing three different waste streams. We went through four versions before landing on this final.

Prototyping wires in Figma:
PUTTING THE PIECES TOGETHER

Seeing as this was a big new feature release for Zabble, I created a video we could use as a marketing teaser in emails and on the marketing website.

Marketing video from prototype:
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