Case Study · Stairtop

Predictive intelligence for every listing.

Real estate agents have been pricing on gut feeling, and it's costing them client trust. Stairtop is the MLS-trained platform I built from scratch — brand, product, and design system — to give agents the evidence to back up what they already know.

Role
Sole designer — brand, logo, product, and design system, end to end. Every prototype is mine.
Built
From scratch — no legacy product or design system to inherit
Now
Expanding into new states to grow beyond our first market
Stairtop listing report showing the pricing simulator and demand analysis
The problem

Agents were pricing on gut feeling.

Real estate agents make some of the highest-stakes recommendations in a client's life — what to list at, when to move — largely on instinct and experience. That's a hard thing to defend when a client pushes back, and it's a harder thing to scale. Client trust erodes fast when "trust me" is the only evidence on the table.

Stairtop's premise: give agents predictive, MLS-trained intelligence for any listing — not to replace their judgment, but to back it up with market evidence the client can see for themselves.

Early framing notes for the seller's report, comp summaries, and brokerage pitch
My own early notes — mapping what a seller's report needed to include and how to pitch it to a brokerage, before a single screen existed.
My role

I built the brand and the product.

As the sole designer, I owned everything: the name, the logo, the color system, the entire product experience, and the design system underneath it. Every prototype that got tested — and every pixel that shipped — came out of my own process, from the first sketch of the icon mark to the final production UI.

Icon exploration for the Stairtop mark, dozens of stair and house motif variations Logo wordmark exploration across multiple typefaces and weights
The icon and wordmark explorations behind the final Stairtop mark — dozens of directions before landing on the one that shipped.
The process

They didn't want more data. They wanted one number.

My earliest prototypes tried to show agents everything at once — every pricing scenario, every band of confidence. In testing, we learned fast that what agents actually leaned on, over and over, was simpler: the days-on-market curve. How long will this sit if we price it here versus there? That's the conversation they have with clients, so that's the number we leaned into — sharpening the pricing simulator until that one curve carried the whole story.

Early dark-mode prototype showing support, listed, most likely, and stretch pricing bands
Early direction — dark mode, more bands and more numbers than an agent needed mid-conversation.
Refined pricing simulator showing a single, clear days-to-pending prediction
Refined direction — one clear prediction, drag-to-explore, built around the number agents actually used.
Notebook sketch of the listings strategy tab and AI recommendation layers
Sketching how the AI-layered recommendation would surface inside the listings strategy tab.
Validation

The evidence agents can actually use.

93%
of Stairtop-associated listings sold faster than the local median
11 days
faster on average than comparable listings
25%
less likely to require a price adjustment
7×
smaller adjustments, on the occasions they do happen
Based on an analysis of Stairtop-associated listings that closed in 2026. Results are observational and do not guarantee future performance.
See it in action

A quick walkthrough of the product.

Outcome

Built from scratch. Now scaling.

There was no existing brand or product to build on — every part of Stairtop, down to the coffee we hand out at events, came out of the same design system. That consistency is part of why agents trust it: it feels like one considered thing, not a patchwork of features.

We're now expanding into new states, taking the same predictive model and brand into markets beyond where we started.

Stairtop-branded mugs and Market Roast coffee bags
Even the swag runs through the same design system — down to the "Market Roast" coffee.
Reflection

Simplicity was the hardest thing to design.

It would have been easy to keep adding bands, charts, and confidence intervals because the model could support them. The real work was figuring out which single number actually changed a conversation with a client — and having the discipline to build the whole product around that number instead of around what the data could technically show.