A trailer and equipment rental marketplace with location-aware search, date-range availability, and programmatically generated SEO landing pages that rank across hundreds of city-and-equipment combinations.

Role: Full-Stack Developer

Type: Rental Marketplace

Stack: Next.js · PostgreSQL · Prisma

Scope: Search · Listings · Programmatic SEO

Someone in Los Angeles searching for a "dump trailer rental" doesn't want options three states away - they want nearby inventory and instant confirmation that it's available on the dates they need. The whole experience hinges on proximity and timing.

That creates two hard problems at once. The marketplace has to surface the right equipment in the right city within seconds, and it has to be findable on Googlefor thousands of long-tail queries like "flatbed trailer rental in San Diego" - searches that no single hand-written page could ever cover.

The search experience is organized around the three things that actually matter to a renter: where they are, when they need it, and what they're after. The bar combines a rental location, a pickup and return date range, and a rental category - then queries inventory by proximity and date availability in one pass.

Category tabs let renters narrow the catalog instantly without re-running a full search, while the date range quietly filters out anything already booked. The goal was to make the common case - "I need this kind of trailer, here, on these days" - feel like a single, obvious motion.

Results render as trailer cards that lead with what renters compare on: capacity, dimensions, and weekly pricing ($650/week and up). Each card is scannable enough to shortlist in seconds, and a click opens a detail page with full specs and a booking flow tied to the selected dates.

Underneath, the hard part was the data model. Equipment, locations, and availability windows are modeled in PostgreSQL through Prisma, so a single query can answer "which trailers of this type are within range of this city and free on these dates?" Getting those relationships right is what makes the search feel instant instead of brittle.

The biggest lever wasn't a feature renters click - it was pages they find. BigRentals programmatically generates a landing page for every city × equipment-type combination, like /locations/us/california/los-angeles/trailer-rentals. One template, fed by the data model, becomes hundreds of pages that each target a specific local query.

Each page ships with structured data, fast static rendering, and clean internal linking between nearby cities and related categories - so search engines can crawl the whole network efficiently and the site ranks for hundreds of long-tail local queries it would never have covered by hand.

Speed isn't just polish here - it feeds directly back into rankings. The SEO landing pages use static generation and ISR so they serve instantly and stay fresh as inventory changes, while image optimization keeps the visually heavy catalog light.

The challenge was keeping large catalog pages both quick and crawlable at scale - fast load times for renters, lean markup and predictable rendering for crawlers. Good performance and good SEO ended up being the same problem solved from two angles.

Big Rentals matches renters to nearby inventory almost as fast as they can type a city, and quietly compounds organic traffic through programmatic SEO in the background. The thing I'm most proud of is how the pieces reinforce each other.

A solid data model, location-aware search, and SEO-driven page generation aren't three separate features - they're one system. Get the model right, and search, listings, and thousands of ranking pages all fall out of it. That's what made building this at scale feel less like brute force and more like leverage.