Live demoReal estate

Aurelia Properties

Conversational property advisor

A property assistant that knows the portfolio it is selling.

Demonstration build

Aurelia Properties is a fictional business, created by Valtair to show how the system works.

At a glance

Type: Demonstration build, publicly reachable
Sector: Real estate (Dubai residential)
Channels: Web chat, embedded in the site and in each listing
Grounding: Structured listing and community records, queried per answer
AI approach: Grounded assistant with tool access to portfolio data
Oversight: Defers to a human advisor outside its scope
Model providers: Multiple, provider-agnostic
Deployment: Cloud, server-rendered with streamed responses
Business: Fictional, built to demonstrate the system

Aurelia Properties is a fictional Dubai estate agency we built to test one question: what does a property assistant need to know before it is worth talking to? Most site chat widgets are a contact form with a cursor. Ayla answers against the actual listing set, holds the requirements a visitor has already given, and hands over with the context intact.

The Aurelia Properties demo homepage: a Dubai property search, with the Ask Ayla assistant in the corner.

The scenario

A buyer relocating to Dubai has a budget and very little sense of what it buys. Agency websites answer that with a filter and a results page, so the visitor is left to work out whether Business Bay or Dubai Marina suits them, what a service charge adds, and which listings are genuinely available. The advisors who could answer that work office hours, and a serious enquiry at 11pm on a Sunday waits until Monday or goes elsewhere.

What it demonstrates

Answers grounded in the live listing set

Ayla answers from the same records the page renders: reference, community, price, beds, baths, area, and handover quarter for off-plan releases. Ask what is available under a budget in a given community and the answer is the portfolio, not a plausible-sounding invention.

Contextual entry from any listing

Every listing card carries its own Ask Ayla button, so the conversation opens already attached to that property. The visitor never has to describe which one they mean, which is where a generic widget loses most of its enquiries.

Qualification before handover

Budget, community, property type, and whether the visitor is buying, renting, or looking at off-plan are gathered in conversation rather than as a form, and travel with the enquiry when an advisor takes over.

Cover outside office hours

The agency publishes Monday to Saturday, 9:00 to 18:00. The assistant covers the rest, which is the practical case for it: the enquiry is captured and qualified while the office is shut instead of arriving cold on Monday.

System architecture

Architecture layer details

Frontend: A Next.js marketing site with a persistent assistant launcher and per-listing entry points.
Backend: A streaming chat endpoint holding conversation state and the requirements gathered so far.
AI: A grounded assistant with tool access to listing and community records, instructed to defer rather than guess.
Retrieval: Structured listing and community data queried directly, so prices and availability are read rather than recalled.
Integrations: Enquiry capture and handover to a human advisor with the conversation context attached.
Infrastructure: Cloud deployment, server-rendered pages, streamed assistant responses.

AI and data components

Assistant grounded in structured listing and community records
Tool calls for listing lookup, filtering, and availability
Requirement capture held across the conversation
Refusal and handover behaviour when a question is outside what the data supports
Provider-agnostic model orchestration

Backend and integrations

Streaming chat endpoint with per-conversation state
Listing and community data as the single source for both page and assistant
Enquiry capture with the conversation attached
Handover to a human advisor during office hours

Engineering decisions

The page and the assistant read the same records

A separate index for the assistant is the fastest way to get an agent quoting a price the page no longer shows. One source removes the drift instead of monitoring it.

Listing facts are looked up, never recalled

Prices, availability, and handover dates come back from a query on every answer. A model asked to remember a portfolio will confidently produce a number that was true last month, and in property that is a complaint rather than an error.

Deferring is a designed behaviour, not a failure

Legal questions, mortgage advice, and anything about a property outside the portfolio route to a human rather than getting an answer. An assistant that will not guess is what makes the answers it does give worth trusting.

Entry from the listing, not only from the corner

A launcher in the corner starts every conversation from nothing. Opening from a listing card means the first message already has its subject, which is most of the qualification done before the visitor has typed.

What this demo does not do

The agency, its advisors, listings, communities, and client quotes are fictional. No property described is real and no figure is a market rate.
The assistant is a single web surface. There is no WhatsApp, voice, or email channel in this build.
There is no CRM behind it. Enquiries demonstrate capture and handover; they do not enter a real sales pipeline.
Viewings are not actually scheduled. Arranging one is represented, not booked.

Capability