Congruent

Congruent

AI native radars for self-driving cars

San Francisco
2 employees
Seed · $500K

About Congruent

At Congruent, we build radars for end-to-end autonomous systems. The most advanced autonomous systems are trained as a single neural network from raw sensor data to navigation actions. For a sensor to be included in these pipelines sensor stacks requires two key properties: access to raw sensor data and a high-fidelity sensor simulator. Current automotive radars have neither, they output heavily processed point clouds and no raw radar simulator exists for driving scenes. Congruent solves both problems: a radar architecture that exposes raw data, paired with a world model based radar simulator. Radar is the only depth sensor at a price point that scales to every car on the road and works in all weather conditions. Congruent is building the radar compatible with the training architectures that will make mass-market vehicles autonomous.

Company details

Team Size2
LocationSan Francisco

Funding

Total raised

$500K

Last stage

Seed

Investors

Y Combinator

Founders

CB

Clement Barthes

ex-head of autonomy at Zendar ex-CTO at Safehub, making smart sensors to evaluate building damage after earthquakes ex-Research Engineer and Lab Manager at UC Berkeley - PEER lab

LinkedIn
Evan Carnahan

Evan Carnahan

Holds a doctorate from UT-Austin in computational science, engineering, and mathematics. Previously spent 1.5 years at NASA JPL and 3.5 years at Zendar progressing to Research Engineering Manager leading radar ML/perception teams. Deep background in signal processing and sensor fusion.

LinkedIn

What happens next.

No applications, no recruiter spam. Just the intro.

01

Confirm the fit

A few questions to make sure this role is the right shape for you. Two minutes.

02

I pitch you to the company

I write the intro, send it to the founder, and handle the back-and-forth.

03

A meeting lands on your calendar

If they’re a yes, I book the chat. You show up — that’s the whole job-hunt.