Arborline

UX DESIGN

STRATEGY

Case study hero image

Overview

Arborline is a map-based quoting platform that helps homeowners visually define tree removal work and receive structured estimates from local arborists.

The idea came from a real project on my own property. After having roughly 20 trees removed, I realized that even with a great arborist, the process still relied heavily on loose descriptions, rough assumptions, and mismatched expectations around scope and cost.

I designed and built Arborline to explore whether a more visual, structured estimation workflow could reduce ambiguity for homeowners, improve quote quality for arborists, and create the foundation for a more scalable local services marketplace.

Role

Founder, Product Strategy, UX Design, AI-assisted Engineering

Timeline

Concept to working product in ~2–3 weeks

Focus Areas

  • 0→1 product strategy

  • Marketplace UX

  • Service design

  • Visual estimation systems

  • AI-assisted product development

  • Business model exploration

The Problem

Tree removal is a high-cost, high-friction service where both sides often lack a clear shared understanding of the work.

For homeowners:

  • It is difficult to estimate cost upfront

  • Quotes often require multiple site visits

  • Pricing can feel opaque or inconsistent

  • Scope is hard to explain through text, calls, or photos alone

For arborists:

  • Many leads are unqualified or poorly scoped

  • Remote assessment is difficult without structured context

  • Estimates often require time-consuming site visits

  • Quote details can be scattered across texts, emails, photos, and notes

Core issue

There is no shared visual language for defining the work.

Last summer, I sent my arborist a rough drawing of my property to explain which trees I thought were part of the job. That drawing became the seed for Arborline: a way to turn informal, ambiguous scope-setting into a structured visual workflow.

Annotated Arborline property estimate map

The Insight

The opportunity was not simply to generate more leads for arborists.

The bigger opportunity was to create a shared estimation layer between homeowners and service providers.

If both sides can see the same map, the same trees, the same scope, and the same assumptions, the quoting process becomes clearer, faster, and more trustworthy.

Arborline turns a rough homeowner description into a structured, visual scope of work.

The Product

Arborline introduces a map-first estimation experience for tree removal.

Homeowners can:

  • Enter an address

  • Recreate their property on an interactive map

  • Place trees visually

  • Tag each tree by type, size, and work needed

  • Indicate whether stump removal is included

  • Receive a rough estimate range

  • Submit the job to local arborists

Arborists can:

  • Review the mapped scope

  • Understand job complexity before visiting the site

  • Add notes, assumptions, and pricing

  • Submit a structured quote back to the homeowner

Key Product Decisions

1. Map-first instead of form-first

Early exploration made it clear that homeowners struggle to describe outdoor work through text fields.

Tree removal is spatial. Location, proximity, slope, access, and grouping all matter.

So the experience shifted from a traditional form-based flow to a map-first interaction model where users could define scope visually.

Why it mattered

  • Reduced ambiguity

  • Made the experience more intuitive

  • Created a shared artifact between homeowner and arborist

  • Made the quote request feel more concrete and trustworthy

2. Progressive disclosure for complex input

Tree work has many variables: tree size, type, access, stump removal, equipment needs, risk, and proximity to structures.

Rather than exposing all of that complexity upfront, I used a progressive interaction model where the user starts with a simple action — placing a tree — and adds detail only when needed.

Why it mattered

  • Kept onboarding lightweight

  • Avoided overwhelming the homeowner

  • Supported more advanced estimation logic over time

  • Created a scalable pattern for future service categories

3. Estimate ranges instead of false precision

A major risk in this type of product is giving users a number that feels overly exact.

Tree removal pricing can vary based on access, equipment, risk, disposal, crew availability, and regional labor rates. A precise estimate would create false confidence.

Instead, Arborline uses estimate ranges and confidence indicators to set expectations without pretending the system knows everything.

Why it mattered

  • Built trust with homeowners

  • Preserved room for arborist judgment

  • Reflected real-world variability

  • Reduced the risk of misleading price expectations

4. Structured quote requests for arborists

The homeowner experience is only one side of the product. For Arborline to work, the arborist experience needed to feel useful rather than like another lead form.

The arborist view gives providers a structured request with visual context, tree-level details, and enough information to decide whether they want to quote, request more detail, or schedule a visit.

Why it mattered

  • Improved lead quality

  • Reduced wasted site visits

  • Made requests easier to evaluate remotely

  • Created a repeatable quoting workflow

5. Marketplace model built around completed value

I explored several monetization paths, including lead fees, premium listings, SaaS tools, and transaction fees.

The strongest model was a transaction-based service fee because it aligns Arborline’s revenue with completed work rather than raw lead volume.

Direction

A 5% service fee on completed jobs creates a cleaner value exchange:

  • Homeowners get easier quote comparison

  • Arborists get better-scoped opportunities

  • Arborline earns when the marketplace creates real value

From Prototype to Product

The initial concept started as a simple question:

Could a homeowner describe tree removal work more clearly with a map than with a form?

I explored several early directions:

  • Text-based quote request

  • Visual property builder

  • Static pricing model

  • Dynamic estimate ranges

  • Linear forms

  • Progressive map-based workflows

The key learning was that the map was not just an input method. It was the core product.

Once the tree placement interaction worked, the rest of the system had a foundation: pricing logic, arborist review, quote comparison, and marketplace mechanics could all be built around a shared visual scope.

AI-Assisted Build Process


Arborline AI-assisted build process interface


This project also became an exploration of how AI-assisted development changes the product design process.

I used AI tools to move from concept to working product quickly, while staying close to the design and product decisions.

Workflow

  • Started with V0 to quickly explore interface direction and flow

  • Moved into Claude Code inside VS Code for more control

  • Scaffolded a Next.js application

  • Built interactive UI components with Tailwind and shadcn

  • Integrated map-based interactions with Mapbox

  • Iterated on product behavior directly in code

Why this mattered

AI did not replace the product thinking. It compressed the distance between product strategy, UX design, and working software.

That allowed me to test interaction models, refine flows, and evaluate the business concept without waiting for a traditional team structure or development cycle.

Business Model Exploration

Arborline has several potential paths to monetization:

  • Lead fee per quote

  • Transaction fee on completed jobs

  • Premium arborist profiles

  • Territory-based visibility

  • Scheduling, quoting, and invoicing tools for arborists

  • Future SaaS layer for local service providers

The most compelling near-term model is a transaction fee on completed work.

This avoids the race-to-the-bottom dynamics of lead generation and instead positions Arborline as infrastructure for clearer scoping, better quotes, and completed jobs.

Outcome

In a few weeks, I moved from a real-world service problem to a working product prototype with both homeowner and arborist experiences.

What I built

  • Homeowner quote request flow

  • Map-based tree placement experience

  • Tree-level scope definition

  • Estimate range logic

  • Arborist review and quote flow

  • Initial marketplace and monetization model

What it validated

  • Homeowners benefit from a visual way to define outdoor service work

  • Arborists need better context before investing time in estimates

  • Map-based scoping can become a shared language between both sides

  • AI-assisted development can dramatically compress 0→1 product exploration

What This Demonstrates

Arborline is less about tree removal specifically and more about how ambiguous offline workflows can be redesigned into structured digital systems.

The project demonstrates my ability to:

  • Identify a real-world service design problem

  • Translate ambiguity into a product model

  • Design both sides of a marketplace

  • Build a working prototype using modern AI-assisted tools

  • Explore business model viability alongside UX

  • Move quickly from insight to execution

Reflection

This project changed how I think about early-stage product development.

The gap between idea, design, and implementation is getting smaller. For design leaders, that creates a major opportunity: we can move beyond static artifacts and use working software to test strategy, communicate vision, and validate product direction much earlier.

Arborline is an exploration of that shift.

It started with a rough drawing sent to an arborist.

It became a working product for turning unclear service requests into shared visual estimates.

Try it out: arborline.co