HopeLink

Stop guessing what you qualify for. We match you to the right transportation programs and help you apply. What inspired us

This started with a pretty obvious but largely unsolved problem: the people who need transportation the most often have no idea what they actually qualify for — or how to get it.

There isn’t a shortage of programs. There’s a shortage of clarity. Every agency has its own rules, its own intake, and its own definition of eligibility. For someone who has stopped driving or is already feeling isolated, that turns into friction fast. Most people don’t push through it.

We weren’t trying to rebuild the system. We wanted to make it usable.

What we built

We built a Smart Match layer that sits on top of existing transportation services and does three things well:

Shows people which programs they may actually be eligible for Explains why they do or don’t qualify Lets them apply to multiple programs at once

Instead of a static list, the system updates in real time as someone adds information like Access ID, Medicaid, or age. You can immediately see your options change.

At a high level, we’re evaluating matches based on:

Match Score=f(service area,accessibility fit,eligibility,service model,reliability)

We also surface why something doesn’t work, which is just as important:

Not in your service area Doesn’t support your mobility needs Requires prior enrollment

No guessing, no dead ends.

How we built it

We focused on something that could actually exist, not just a concept.

Built a structured model for riders, programs, and trip requests Simulated a matching engine based on real constraints Designed a guided intake that doesn’t overwhelm the user Added real-time re-ranking when eligibility changes Kept the UI simple, readable, and accessible

It’s designed to plug into existing systems, not replace them.

What we learned

This isn’t really a transportation problem. It’s an access problem.

People don’t need more options. They need to understand which options apply to them. And they need to understand it quickly.

We also learned that explaining why something doesn’t match is just as important as showing what does. That’s where trust comes from.

Challenges we faced

The biggest challenge was balancing complexity with usability.

The real system is messy. Multiple agencies, overlapping rules, inconsistent requirements. We had to simplify that without losing what actually matters.

We also had to be careful not to drift into “ride share” patterns. This isn’t about booking a ride. It’s about navigating eligibility and getting into the right programs.

And practically speaking, we had to simulate intelligence without live integrations, which meant being very intentional about how we structured and displayed the matching logic.

Where this goes next

With deeper integration, this could:

Submit applications directly to multiple programs Track status across agencies Continuously update eligibility Identify gaps in service coverage