Conversational commerce

Aurelia: Planning and booking a stay through conversation

Aurelia keeps room discovery, dining, events, rewards, and checkout in one conversation. Guests can move from an open request to a confirmed booking without jumping between separate flows.

AudienceB2B2C

DomainHospitality · AI

RoleSolo product designer

PlatformMobile

Year2026

ScopeCase study and prototype

Reading time4 min

View the product

The full conversational booking flow, room discovery, and cross-sell experience are built and interactive.

Open Figma prototype

30 Second Summary

OWNERSHIP

I designed the booking flow, competitive analysis, guest journey, and core product screens.

CORE DECISION

Bring structured checkout into the conversation so intent can become booking in one flow.

EVIDENCE

Competitive mapping, guest archetype, and a complete conversational commerce solution visual.

Overview

A conversational hospitality booking concept.

Aurelia keeps room discovery, dining, events, rewards, and checkout in one continuous thread. I defined the conversational booking flow, competitive framing, guest journey, and core product screens.

Problem

A single guest request had to survive discovery, availability, rewards, and checkout.

Guests had to translate one goal across discovery, filters, availability, rewards, and checkout. The design needed to preserve context as an open request became a structured booking decision.

Discover to checkout

The conversation changes as intent becomes commitment.

Discovery stays open while the guest explores. As intent becomes specific, the same thread introduces choices, confirmation, and checkout without forcing a new navigation model.

Product direction

Aurelia was reframed around the travel planning sequence: intent, comparison, commitment, and adjustment. The product needed to keep the guest oriented as they moved between rooms, dining, events, rewards, and support.

The solution

Ask, act, and confirm inside one conversation.

The service keeps context as a guest moves from an open request to a booking decision. The prototype demonstrates discovery and checkout; recovery and handoff flows are not demonstrated.

01 · ASK

Clarify only when the answer changes the options.

The conversation begins with natural language, then narrows dates, preferences, or budget only when that information materially affects availability.

02 · ACT

Surface a small set of bookable choices.

Two or three options preserve price, timing, and service context without turning the conversation into a filter form.

03 · CONFIRM

Keep the selected experience and next action together.

The in-thread summary preserves the chosen room or experience, dates, and commitment before the guest proceeds.

Trade offs and validation

The main trade off was balancing inspiration with booking certainty.

I chose AI suggestions that remain editable. The product anticipates, but the guest can still change dates, room type, budget, and event preferences without restarting.

Personalization vs control

Aurelia asks one clarifying question only when it can materially change the room, dining, or experience options that follow.

Inspiration vs utility

Visual discovery should build excitement without turning the flow into moodboard browsing. I kept every recommendation tied to a bookable option.

Planning depth vs speed

A full itinerary builder could become heavy. I chose a lighter conversation model that handles one decision at a time and preserves context across services.

Reflection

Restraint keeps the guest moving

Hospitality products can become noisy quickly. This project pushed me to remove anything that did not help the guest choose, confirm, or change a booking. The quieter the interface became, the easier it was to keep the conversation focused on the stay.

Open Figma prototype

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