The build, in the open.

Ceres is queer owned, woman owned, neurodivergent and disability friendly. We recognize the gaps in care for these communities, and we're building with that in mind.

Ceres is the first product of Urania.Systems, a technology company dedicated to building tools that respect the time and intelligence of the people who use them.

Founder context and domain expertise

I will be a retired hairstylist of 11 years as of my 30th birthday, July 11, 2026. I created Ceres because I had no choice.

"I built Ceres because I got hit by a truck. No booking app was built for the reality of running a business inside a human body that sometimes breaks down. Stylists deserve a tool that gives them permission to be human."

I have built a fully working demo and am in the research portion of my build. I have decided to build the full app alongside my college courses and education, utilizing this research to further develop my skills. By honing in on the problems I am an expert in, I also hope to discover new issues that I may not have been aware of.

The GIS use case

Some questions I will be looking into include:

• Where are the underserved independent booth renter stylists in the Myrtle Beach / Horry County area? I expect to see some density at least in the three Sola Salon locations, my business has been based at the Surfside branch.
• Why does this geography matter in this business? I plan on piloting to the Sola locations I previously mentioned. Need to find what else I am missing.
• How does this map become a tool for market validation? By identifying my ICP and being able to potentially interview desired clients for this app.
• Personal curiosity: if 1 out of 2 salons go out of business in the first two years, why is it so difficult to find board compliant rental spaces?

Data and layers

Planning to include these layers, adding and retracting as I gain further insight:

• Salon / booth rent locations (point layer)
• Single-female-headed household density (demographic layer), with a second layer for number of dependents (choropleth)
• Median income by census tract (reference layer)
• Zip code / road boundaries (reference layer)

Four GIS concepts

• Feature classes and geodatabases (Ch 4)
• Attribute tables and spatial queries (Ch 3)
• Thematic / choropleth mapping (Ch 2)
• Coordinate systems and data sources (Ch 1–5)

Justification and hypothesis

The main question: do geographic concentrations of independent booth renter stylists overlap with census tracts showing higher rates of single female headed households and lower median incomes after expenses? If so, does that intersection represent the highest need early user group for Ceres?

My hypothesis: independent booth rental stylists operating in areas with higher concentrations of single-female-headed households are more likely to experience acute financial instability from income disruptions. If that demographic overlap is spatially concentrated in specific areas of Horry County, those zip codes represent the highest-need early adopters for Ceres, validating both the product's core feature set and its initial geographic launch strategy.

What the GIS will test:

• Whether stylist density and single mom household density spatially correlate in Horry County
• Whether these overlapping areas also show lower median incomes, suggesting higher financial pressure
• Whether proximity to tourist corridors affects cancellation risk and income stability

Expected outcome: the map will either confirm or challenge the assumption that Ceres' target user is geographically concentrated in specific zip codes, giving the product a data backed foundation for a hyper local launch strategy, marketing spend, and investor pitch materials.

Other closing thoughts

I would like to include the disability layer in this research, however I believe that may need to be separate. Other issues that come with veteran hair careers include chronic injury, medical debt, gaps in insurance, arthritis, even chemical exposure. I have experienced all of these barriers myself.