The Booking Engine
Ruby GmbH · Workspaces · 2026
A self-serve booking engine for Ruby Workspaces, replacing a staff-gated request flow. Customers pick a date, time and party size, see real-time meeting-room availability, reserve the room they want, and check out, without waiting for anyone to get back to them.
Year
2026
Role
End-to-end UX · Interaction Design
Team
Product Manager · Engineers
Platform
Desktop & Mobile
The problem
Booking a meeting room at Ruby required a person on our side before anything could happen. A customer filled out a request form and waited for us to get back to them, or called in directly. Either way the reservation depended on staff being available and on a round of back-and-forth. The interface was built around our internal process, and customers paid for it in wait time.
The flow served our operations more than it served the customer standing in front of it.
What I built
Before designing the flow, I benchmarked how competitor booking tools handled real-time availability, and audited Nexudus, the platform the engine runs on, to separate real technical constraints from assumptions. Nexudus made in-flow payment too large a lift for the timeline, so I designed payment as a deliberate handoff outside the engine. The flow itself is built around the customer's actual question: is there a room for my group, on this day, at this time? The customer enters date, time and number of guests, and the engine returns only the rooms available for that exact slot. They pick one, move into a personal-details step, and complete the reservation. Today it covers meeting rooms, with the same flow built to extend to day passes, dedicated desks and add-on services.
Booking panel Enter date, time and party size; only the rooms free for that exact slot come back.
Room selection The customer picks from real availability and moves straight to reservation.
AI in the process
I used AI to speed up the groundwork: benchmarking how competitor booking tools handled real-time availability, and parsing Nexudus's documentation to separate real technical constraints from assumptions faster than manual review would allow. What to build was still my call.
Outcome
In its first weeks of tracking, the engine took 23% of all meeting-room bookings. Those bookings ran about 22% higher in average value than staff-handled ones, so the channel already accounts for 27% of meeting-room revenue.
Of all bookings
Of revenue
Value per booking