Overview
In Beta
0→1 AI rental agent
more conversions, less work
Overview
Overview
Overview
I designed a fully transactional AI rental agent, treating AI behavior as a core design material rather than a UI layer.
By prototyping directly with LLMs and designing the conversational architecture, privacy model, and checkout flow, I enabled the system to safely capture leads, complete payments, and drive conversions under real-world constraints.
Product
web
Timeline
3 Weeks
Skills
End-to-end product design
AI interaction design
LLM prototyping
System logic + UX
My Rolle
Lead Designer
Team
AI Engenering Architect, Data PM
Overview
Context & Constraints
Existing chat solutions can’t complete rentals, process payments, or handle operations—blocking revenue and trust.
Prototyping with LLMs: De-risking Feasibility
Challenge
The challenge was designing an AI that could reliably interpret storage-specific intent, execute multi-step tasks, and drive revenue, not just respond.
Testing
I treated the LLM itself as a design prototyping tool. Before creating UI, I ran conversational experiments to test:
Industry-specific intent recognition
Sentiment detection
Multi-step task completion
Upsell timing and phrasing
Outcome
By validating AI behavior first, the product foundation supported complex rental tasks, enabling a conversational system capable of completing bookings instead of simply answering questions. Targeting primary metrics:
Intent resolution rate ↑
Successful outcomes per session ↑
Chapter 2
Designing for Trust, Privacy, & Flow
Challenge
Rental transactions require users to provide sensitive information, including payment details and personal data. Directly exposing this data to an LLM created security, compliance, and trust risks.
Design Decision
I designed custom UI widgets to securely capture sensitive inputs while implementing an event-based logging system that communicated completed actions to the AI without exposing raw data.
Trade-Off
This reduced conversational flexibility and removed some “natural chat” moments. However, it increased security, trust, and scalability.
Outcome
The AI could safely support payments and reservations while maintaining user confidence and meeting production-level privacy expectations.
