Brokerage Growth AI

Brokerage Growth AI

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Goal & Context

GYDE Health is an AI-driven brokerage enablement platform helping brokers uncover revenue opportunities.

  • Launched AI-powered Gap Scores to surface personalized recommendations.

  • Designed a cross-sell engine that guides brokers on what to sell and to whom.

  • Made sure the AI doesn’t just talk, it listens. Broker feedback directly improves future recommendations.

  • Operated in a fast-paced startup environment, adapting the Design System, re-skinning to the brand manual, and partnering closely with engineering to ship quickly.


This wasn’t just feature design. It was about turning AI intelligence into confident business action.

Designing AI for Action

The core challenge:

How do you make AI insights understandable, trustworthy, and actionable for brokers?

  • Gap Score visualization

  • Client-level opportunity breakdowns

  • Cross-sell recommendations

  • Timing signals (when to reach out)

  • Actions after knowing the recommendation


The design focus:

  • Turn complex AI scoring into clear decision support

  • Reduce cognitive load

  • Balance transparency vs. simplicity


AI didn’t replace broker judgment, it amplified it.

AI-Assisted Design Workflow

This project pushed my AI workflow further.

Tools & Methods:

  • Cursor for rapid prototyping and logic exploration

  • Meta prompting to refine AI interactions and edge cases

  • Figma Make for rapid UI iteration

  • Daily AI testing loops to validate assumptions

Meta prompting became essential, designing not just screens, but the language layer between AI outputs and user trust, this fast work helped me have more conversations in what we can deliver and timeline. After this I need to also think about multiple escenarios and have a proper hand off to devs.

I used AI as a thinking partner.

This project pushed my AI workflow further.

Tools & Methods:

  • Cursor for rapid prototyping and logic exploration

  • Meta prompting to refine AI interactions and edge cases

  • Figma Make for rapid UI iteration

  • Daily AI testing loops to validate assumptions

Meta prompting became essential, designing not just screens, but the language layer between AI outputs and user trust, this fast work helped me have more conversations in what we can deliver and timeline. After this I need to also think about multiple escenarios and have a proper hand off to devs.

I used AI as a thinking partner.

Startup Velocity & System Thinking

GYDE AI was an early-stage product. Speed did mattered.

We used:

  • shadcn/ui as a base design system

  • Heavy re-skinning and personalization to align with product vision

  • Component restructuring to support AI-specific UI patterns

Because timelines were tight:

  • I made fast decisions with high ownership

  • Prioritized ruthlessly

  • Balanced short-term delivery with scalable foundations

  • Joined daily standups to surface blockers immediately

  • Learn the business fast and identify leverage points

  • Design under uncertainty and lead

    https://gydehealth.ai/

GYDE AI was an early-stage product. Speed did mattered.

We used:

  • shadcn/ui as a base design system

  • Heavy re-skinning and personalization to align with product vision

  • Component restructuring to support AI-specific UI patterns

Because timelines were tight:

  • I made fast decisions with high ownership

  • Prioritized ruthlessly

  • Balanced short-term delivery with scalable foundations

  • Joined daily standups to surface blockers immediately

  • Learn the business fast and identify leverage points

  • Design under uncertainty and lead

    https://gydehealth.ai/

My motto: Driving clarity and momentum through collaborative, accountable leadership.

Sounds easy but takes a lot from a Senior Lead Product Designer. 🚀

Discovery & Designs

Discovery & Designs

This case study includes selected Design System components, product explorations, discovery work, presentations, and system diagrams, a curated glimpse into the broader strategic work behind Connect.

Visit Figma Make Draft

Post-Launch Impact

Designing AI products is different.

You’re not designing static flows, you’re designing around evolving intelligence.

After launch:

  • The foundation allowed continued iteration on scoring logic

  • The cross-sell framework became extensible

  • AI outputs could scale across broker portfolios

Most importantly, the product shifted from “data display” to decision enablement.

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Designed by Ingrid Valdera

Last updated February 2026.

Designed by Ingrid Valdera

Last updated February 2026.