A design challenge for Smartness, not shipped. SmartChat is a guest messaging platform for short-term rental hosts. Given a brief to design its main chat screen, I reframed the problem: the goal isn't a better inbox, it's shifting the operator's role from managing every message to handling only the exceptions. AI takes the predictable; the human takes what matters.
Hosts managing short-term rentals live in their inbox. Messages arrive from Airbnb, Booking, WhatsApp, and direct channels, each tied to a specific guest and reservation, and a busy operator can be juggling 50+ conversations at once.
The brief asked for a main chat screen that was usable, efficient, and scalable. But “design a better inbox” is a solution, not a problem. Before designing anything I wrote down the assumption I'd be working from:
“SmartChat's underlying goal is to shift the operator's role from inbox manager to exception handler. AI handles the predictable. The operator steps in only when it truly matters.”
I labelled it explicitly as my interpretation of the brief, not a stated objective, and said I'd be happy to be challenged on it. Naming an assumption out loud is more useful than hiding it inside a design.
Time was limited, so I spent a meaningful part of it not designing.
With a property manager running 12 properties across Airbnb and Booking. I asked about moments, not features: the last time he missed a message, how he handles last-minute problems, whether he'd trust AI to send without reviewing first.
Across six PMS and guest messaging tools (Guesty, Hostaway, Hospitable, Lodgify, Asksuite, Alliants) to find the common patterns and where they all stopped.
On r/ShortTermRentals, I posted to collect firsthand signal and analysed four existing threads with 200+ comments from hosts at different scales, using AI to help synthesise.
“I woke up to 5 guest messages and had no idea which one was urgent”
Hosts manage 50+ chats. Speed matters, but so does knowing who you're talking to. One host described waking up to five guest messages with no idea which was urgent.
“I'd let AI handle stuff at night, but during the day I want to see what goes out”
AI can handle most messages, but hosts fear losing their voice. My interviewee put it precisely: he'd let AI handle things at night, but during the day he wanted to see what went out. Too much control defeats the purpose; too much autonomy breaks trust.
“I stopped using auto-replies because guests kept complaining they felt like talking to a machine”
Guests expect instant replies, but automated reads as robotic. A recurring sentiment across four Reddit threads: hosts abandoning auto-replies because guests complained they felt like talking to a machine. Small hosts built their brand on personal touch.
Rather than paper over the gaps in the brief, I wrote the assumptions down as a first-class artifact:
I also flagged the edge cases I couldn't resolve: a guest writing from different channels about the same reservation, or referencing a future booking inside a current thread. Merging by reservation rather than channel puts real matching logic on the backend.
These were informed assumptions, not certainties. Saying so is the point.
One screen, three panels, each answering a specific challenge: the inbox (scan, prioritise, switch), the conversation (read, respond, collaborate), and the context panel (understand the guest, control the AI).
A NEEDS ATTENTION section surfaces only conversations requiring human action: urgent issues and AI messages awaiting approval. Everything else stays accessible without competing for attention. Semantic badges (URGENT, PENDING APPROVAL) tell the host why a conversation needs them, not just that it does, so triage happens without opening a single chat. Opening one reveals the guest panel and automation timeline: full context, zero digging.
Instead of one global autonomy setting, day/night modes mirror how hosts actually behave: during the day AI suggests and the host approves; at night AI auto-sends and the host reviews in the morning. Every AI message offers three explicit choices: Send / Edit / Ignore. Nothing goes out without either a decision or a deliberate delegation.
And rather than asking for trust upfront, the system earns it with evidence: it tracks how often the host sends AI suggestions unchanged, and when the data shows 94% approved, it nudges toward Autopilot.
A single global autonomy toggle: the obvious, one-setting version of this control. It fails the moment you notice hosts don't have one autonomy preference, they have two: a daytime one and a nighttime one. A toggle that can't express that isn't simpler, it's just wrong for half the day.
A tone slider with live preview replaces a binary formal/friendly toggle with a continuous spectrum, and what the host sees updating in real time is exactly what the guest gets. Inline translation with a language indicator keeps speed high across languages. Internal notes let teammates leave context inside the conversation (“this guest had issues last time, handle with care”), which improves both the human and the AI response.
Templates are a starting point, not a cage: the AI surfaces the most relevant one already adapted to the property's tone of voice, dynamic variables fill automatically, the host personalises, and the guest sees no trace of a template. Multi-reservation handling keeps each stay as its own thread while linking them visually: context switches, the conversation doesn't. The three-panel layout was built to collapse: each panel becomes a full mobile screen, same hierarchy, different viewport.
Beyond the obvious validation work (usability testing with real hosts, tracking adoption, measuring how the autonomy model performs), three strategic directions:
AI flags likely issues before the guest writes: late check-in patterns, weather disruptions, recurring complaints.
Each property already has its own tone, timeline, and autonomy settings. Make that a first-class feature: pick a goal (“maximise reviews”, “minimise effort”), and the whole communication stack auto-configures.
The 94% nudge becomes a trust score that progressively unlocks AI capability and correlates with retention and expansion.
If this went live, here's what would tell me whether the reframing actually worked:
Whether hosts actually spend less daily time in the inbox once AI handles the predictable share.
How many hosts who cross the 94%-approved threshold actually accept the nudge, versus staying manual out of habit.
Whether hosts actually run different autonomy day vs. night, or converge on one setting, which would undercut the whole premise of the decision.
This was built on my own research, my own instincts, and a limited window into the problem. It's a strong starting point designed to evolve: with real product data, user behaviour, and a team's context, every assumption here becomes a conversation and every decision gets sharper.