Groundskeeper

Inspiration

Two of us have years of lawn and landscape care experience, and we've seen firsthand how hard it is to scale that kind of business. We figured other owners had the same problem, so we talked to several medium-size and large landscapers. They all described the same thing: their operations are scattered across too many tools. Companies with crews of 50+ people and 100+ clients struggle to keep track of which crew is doing what, because scheduling, pricing, routing, and project costs each live in a different place.

What it does

Groundskeeper puts a landscaping company's operations in one workspace that even a less business-savvy owner can manage.

Plan the day and week: assign visits on a crew-by-week board that checks shifts, breaks, crew availability, customer time windows, and road travel.
Suggest a schedule: propose a Monday–Friday plan within crew shifts and team size.
Price jobs: suggest prices from service time, crew size, travel, materials, equipment, overhead, and target margin. The owner always reviews the price.
Route crews: show customer stops and road-following crew routes on a map, next to the local forecast.
Track project profit: reconcile budgets, actual spending, and outstanding commitments, with forecast profit and margin updating as the numbers change.
Import customers: bring in jobs from Excel or CSV, or add them by hand.
Keep data private: each owner signs in to their own business workspace.

Live demo: https://groundskeeper-nine.vercel.app Code: https://github.com/mayweajh-collab/Groundskeeper

How we built it

We built most of it with ChatGPT Codex and used Perplexity for research. The backend is Node.js running as a Vercel function, with Clerk for sign-in and Neon Postgres for private business workspaces. The frontend is a custom dashboard with Leaflet maps. Under the hood it uses:

the U.S. Census Geocoder for address lookup
OpenStreetMap/OSRM for driving times and routes
Open-Meteo for the weather forecast

All money math is done in exact cents. The demo runs on mock customer data.

Challenges we ran into

Most of us don't come from a technical background.
We didn't really understand how API keys work at first.
It was hard to understand what the backend of a process actually does and how our operations system plugs into it.
Our UI wouldn't change the way we wanted, and it's still fairly basic.

A lot of it was trial and error: testing what worked and what didn't.

Accomplishments that we're proud of

We're proud of the product we built, and of how much we learned in 24 hours. We all know far more than we expected to after this hackathon.

What we learned

How APIs work
Connectors
Skills
UI/UX and dashboard design
Starting from a real client base, even a fictional one, gives you an ideal customer and makes the problem feel real.
How to combine features from different sites and tools to get the interface we wanted.

What's next for Groundskeeper

This version is built for landscaping companies. The principles behind it could carry over to other traveling, service-based businesses with similar models, like HVAC or plumbing.