Fogo Island Inn
Hotels
KHDS Synthesis Build 02
Avenlo House
What happens when hospitality technology starts with the guest instead of the software?
Avenlo House is a fictional hospitality system developed by Kodawa Labs from research across ten hospitality institutions. It combines hotel stays, restaurant reservations, events, membership, AI guest assistance and operational decision support into one connected experience.
I build digital systems that help hotels and restaurants attract more guests, own more bookings, and deliver better service.
01 / Research
The research
Ten institutions. Different sectors. One question.
Cases 21-30 examined how different hospitality businesses manage discovery, booking, arrival, experience, access, service recovery and return.
The purpose was not to copy any single property. It was to identify recurring structural problems that appear when hospitality becomes more complex.
Sky Lagoon
Wellness
Reffen
Venues
Time Out Market Lisboa
Venues
Generator Copenhagen
Hostels
Scarlet Lady
Cruises
Soho House Toronto
Members' clubs
The Arts Club London
Members' clubs
Casa Cipriani New York
Members' clubs
The Social Hub Amsterdam City
Mixed-use hospitality
02 / Findings
What the research taught us
One property can contain many relationships.
A hotel guest, diner, event organizer and member may interact with the same institution while having different access, capacity, expectations and commercial journeys.
Shared institutional identity ≠ uniform entitlement
Physical co-location ≠ uniform access
Hotel stay ≠ membership
Membership benefit ≠ guaranteed capacity
Public space ≠ controlled workspace
Physical departure ≠ relationship closure
Instead of forcing everybody through one generic hospitality funnel, the system needs to understand why someone is here.
03 / Product
What we built
Research translated into product decisions guests and operators can actually use.
Intent-based guest journeys
Stay, Dine, Gather and Belong route people according to what they are actually trying to do.
Business problem
Hospitality websites often mix rooms, restaurants, events and membership into one confusing customer journey.
Real-property implementation
A website architecture that gives each revenue stream its own conversion path while preserving one property brand.
Direct booking conversion
The Stay journey moves guests toward room availability while keeping room inventory distinct from other property capacity.
Business problem
A property may attract demand but still lose direct bookings when its own booking journey is weaker than an OTA.
Real-property implementation
A conversion-focused hotel website connected to the existing booking engine or PMS with first-party analytics.
Independent dining reservations
Luma operates as its own hospitality destination with separate restaurant availability.
Business problem
Hotel restaurants are often presented as amenities rather than independent revenue-generating experiences.
Real-property implementation
Dining discovery, menu presentation, direct reservations and appropriate hotel-to-restaurant cross-selling.
Qualified event enquiries
Gather treats meetings, launches, dinners and private events as high-value sales journeys.
Business problem
Generic contact forms provide sales teams with too little information about potentially valuable event leads.
Real-property implementation
Event flows capturing event type, dates, party size, requirements and commercial context before staff follow-up.
Contextual AI guest assistance
The assistant answers from bounded property knowledge and escalates uncertain or sensitive requests instead of inventing certainty.
Business problem
Guest teams repeatedly answer routine questions while poorly governed AI can create service failures by promising things the property cannot deliver.
Real-property implementation
A property-grounded concierge with approved knowledge and clear human escalation rules.
Operator decision support
The operator console surfaces arrivals, room readiness, service exceptions, access context, event activity and commercial opportunities.
Business problem
Important information is frequently scattered across PMSs, reservation systems, emails, spreadsheets and staff knowledge.
Real-property implementation
A lightweight operations layer that prioritizes what requires attention instead of creating another dashboard full of noise.
04 / AI
Operating principle
AI when it knows.
Human when it matters.
Context preserved either way.
Avenlo deliberately avoids unsupported certainty. Sensitive, operational or unclear requests can become contextual handoffs rather than fabricated answers.
05 / Translation
Research to business value
Every feature needs a reason to exist.
Research problemAvenlo responseReal-property implementation
Multiple guest intentsStay / Dine / Gather / BelongIntent-based website architecture
OTA dependenceDirect booking conversionBooking-engine and PMS integration
Dining treated as an amenityIndependent Luma journeyDirect restaurant reservations
Weak event enquiriesGather qualificationEvent lead-generation workflow
Repetitive guest questionsAvenlo AIProperty-grounded concierge
AI uncertaintyHuman handoff with contextStaff escalation workflow
Fragmented staff informationOperator consolePMS / CRM / operations dashboard
Hidden revenue opportunitiesUpgrade and cross-sell promptsAutomated revenue workflows
06 / Commercial system
What the system is designed to do
Attract. Convert. Serve. Retain.
Bring more qualified guests into the property ecosystem.
Stronger hospitality presentation, clearer positioning, intent-aware navigation and search-ready property content.
Own more of the booking relationship.
Direct rooms, dining, events and membership conversion with first-party analytics and appropriate cross-selling.
Give guests useful help without removing hospitality.
Contextual AI, human escalation, pre-arrival support and better operational visibility.
Let the relationship continue beyond departure.
Guest preference continuity, CRM integration, return offers and future direct-booking opportunities.
07 / Real property
What Kodawa Labs could implement
A system built around the property, not another software stack.
A real deployment would begin with the property's guest journey, commercial model, existing systems and operational constraints.
Digital acquisition
- Hospitality website redesign
- SEO and structured property content
- Restaurant and experience discovery
- Campaign landing pages
- Analytics and conversion tracking
Direct conversion
- Direct booking-engine integration
- Restaurant reservation integration
- Event enquiry systems
- Upsell flows
- Membership or loyalty acquisition
Guest intelligence
- CRM integration
- Guest preference capture
- Pre-arrival communication
- Stay context
- Post-stay relationship building
AI guest experience
- Property knowledge assistant
- FAQ automation
- Pre-arrival concierge
- In-stay assistance
- Human escalation with conversation context
Operations
- PMS, reservation and service signals
- Arrival exceptions
- Room-readiness visibility
- Service requests and event handoffs
- Revenue opportunities and decision support
The goal is not five disconnected pieces of software. It is one digital hospitality system built around the guest journey and the property's commercial model.
08 / Evidence boundary
What Avenlo proves
Research can change what gets built.
Avenlo House is a fictional research synthesis prototype. It demonstrates how KHDS research can be translated into product architecture, interaction design and operational logic.
It is not presented as:
- a live hospitality property;
- a commissioned client deployment;
- evidence of measured revenue improvement;
- a live PMS or reservation implementation;
- a new KHDS validation case;
- proof of a specific ROI.
A real implementation would require property-specific discovery, integrations, security review, operational testing and measurement.
Kodawa Labs
Research is useful when it changes what you build.
I'm Brian Kodawa, founder of Kodawa Labs. I study how hospitality businesses attract, convert and serve guests, then translate those findings into websites, booking systems, AI experiences and operational tools.
I build digital systems that help hotels and restaurants attract more guests, own more bookings, and deliver better service.