AI Appetite checker

Ai appetite checker results page

Key results

• Lifted quote conversion from 29.7% to 38.9% (+9.3 percentage points) • Designed natural-language risk input, so agents describe a risk in plain language instead of filling a rigid form • Built confidence-tagged recommendations, framed as "likely in this carrier's appetite" rather than a guarantee • Tested and dropped a Kanban-style tracking board after research showed agents didn't need it, keeping the product simpler and faster to ship

Role and timeline

• Lead Product Designer • 2025 - present

Methods or tools

I ran interviews with ADMs and agents, prototyped alongside the research, and used Granola and NotebookLM to turn call recordings into design decisions.

Agentero Appetite Checker 'Get carrier recommendations' screen with line-of-business and state filtersAppetite Checker results ranking carriers Layers, Hourglass, Catalog and Quotient, with a chat panel on Layers' requirementsRisk description field with automatic strength evaluation, rated 'Great risk description'Risk description field rated 'Poor risk description' with suggested details to addLayers carrier chat panel answering a roof-age underwriting question, with guideline source documentsImpact summary: converted users up from 186 to 218 and conversion rate up from 29.7% to 38.9%

Brayan Angulo

©2026

Brayan Angulo

©2026

Brayan Angulo

©2026