Hey everyone 👋, we are Ryland and Alexander, co-founders of Dream.

TL;DR: Dream automatically photographs every vehicle or machine that passes through your gate, builds a 3D condition record, and uses AI to catch new damage the moment an asset passes through.

Launch Video: https://youtu.be/uMP12lCb12E

The problem

Damage is an expensive leak for anyone who moves large, expensive assets in and out all day. It shows up two different ways:

Car dealership service departments

• As a car pulls into the service entrance, damage that could be identified and fixed goes unnoticed, which is lost revenue.
• Without a record of the car's condition on arrival, disputes come down to the customer's word against the dealership's, and the dealership gets blamed for pre-existing damage.

High-value asset rentals (heavy equipment, trucks, boats, planes, trailers)

• Teams are inconsistent about photographing assets as they go out and come back, so new damage gets missed before the customer is billed.
• When damage is missed, there's no evidence to charge the customer, so the company eats the repair cost out of pocket.

What Dream does

Dream cameras sit at your gate, lane, or service drive and turn every pass into evidence, automatically:

• Continuous capture: every vehicle or machine that moves through is photographed from every angle. No one has to remember to do it.
• Asset identification: Dream knows exactly what you need to look at.
• 3D reconstruction: every pass becomes a complete 3D condition record for that asset.
• AI damage detection: on return or intake, Dream compares against the last record and flags new dents, scrapes, and visible changes right away.

The result is one clear source of truth for every asset's condition over time: rental companies catch damage before they bill so they stop eating repair costs, and dealership service departments find more fixable damage and resolve customer disputes with evidence.

Works across cars, vans, trucks, heavy equipment, and more.

Why us

We have spent years training models for aerial imaging and edge computer vision devices. Ryland has worked on the exact problem of detecting damage to assets from aerial imaging, such as degraded paint. Alexander has trained models that run on edge cameras deployed in the wild in Africa and India to detect poachers.

Our ask

We're onboarding partners now. We'd love intros to:

• Car dealerships
• High-value asset rental companies (heavy equipment, trucks, boats, planes, trailers)

If you run one of these, or know someone who does, please message us on LinkedIn!