Natera
optiFLOW: Validating AI-Generated Document Tags
optiFLOW: Validating AI-Generated Document Tags
As a UX Designer partnering with the Senior Product Lead for Billing Experience, I designed a clearer workflow for reviewing AI-generated document tags, supported by presentation assets, system diagrams, and final UI.
Explore the prototype below by adding or removing a tag. The design system documentation is embedded in this recreated prototype.
My Role
Product Designer
Team
UX Designer (me), Senior Designer, Engineer, PM
Stack
Figma, Claude, Gemini, Miro, UX Thinking, Figma Make
Company
Natera
Industry
Biotechnology
Timeline
2 weeks
My Role
Product Designer
Team
UX Designer (me), Senior Designer, Engineer, PM
Stack
Figma, Claude, Gemini, Miro, UX Thinking, Figma Make
Company
Natera
Industry
Biotechnology
Timeline
2 weeks
Introduction
Introduction
The existing review process relied on fragmented manual steps, making AI-generated tags slower to validate and harder to correct. Working with the Senior Product Lead for Billing Experience, I explored a clearer review screen and extended the platform with new tools, components, and interaction patterns.
New tools: Controls for accepting, removing, correcting, and validating tags
New documentation: Component behavior, variables, states, and edge cases
Team alignment: A shared reference for product, design, and engineering
If implemented, the proposed workflow could reduce review time and rework while improving tag consistency and document throughput. These outcomes are hypothetical and would need to be validated through usability testing, workflow analytics, and production data.
read more about my process, design documentation, and designing around assumptions
The existing review process relied on fragmented manual steps, making AI-generated tags slower to validate and harder to correct. Working with the Senior Product Lead for Billing Experience, I explored a clearer review screen and extended the platform with new tools, components, and interaction patterns.
New tools: Controls for accepting, removing, correcting, and validating tags
New documentation: Component behavior, variables, states, and edge cases
Team alignment: A shared reference for product, design, and engineering
If implemented, the proposed workflow could reduce review time and rework while improving tag consistency and document throughput. These outcomes are hypothetical and would need to be validated through usability testing, workflow analytics, and production data.
view case study deck here
The existing review process relied on fragmented manual steps, making AI-generated tags slower to validate and harder to correct. Working with the Senior Product Lead for Billing Experience, I explored a clearer review screen and extended the platform with new tools, components, and interaction patterns.
New tools: Controls for accepting, removing, correcting, and validating tags
New documentation: Component behavior, variables, states, and edge cases
Team alignment: A shared reference for product, design, and engineering
If implemented, the proposed workflow could reduce review time and rework while improving tag consistency and document throughput. These outcomes are hypothetical and would need to be validated through usability testing, workflow analytics, and production data.