The trusted sensor simulation and reconstruction software to test and validate Physical AI.

Parallel Domain was founded in 2017. We built procedural simulation environments at a scale and quality the autonomy industry hadn’t seen, and our customers developed real systems on the back of it. Our approach has shifted from procedural generation to reconstructive simulation: turning real-world capture into geometrically accurate, physically valid, semantically labeled, HD-mapped digital twins. The product is PD Replica. It turned out to be more scalable and more performant than what we had before, and it has fueled a year of fast growth across testing and validation.

The bet was a version of what AI researcher Richard Sutton calls the Bitter Lesson: in the long run, generalized systems that learn from real data beat anything you can hand-program. We had spent six years writing human logic to generate worlds. The right move was to let go of it and let the data do the work. The same shift is now playing out across autonomy itself, as customers move from modular perception-planning-control stacks to end-to-end learned models that demand far more, and far better, simulation than the old toolchain could deliver. PD Replica was built for that world.

Today, the largest autonomy programs in the world rely on Parallel Domain to engineer the data their systems need and to validate their simulations against the real world before fleets touch a public road. The aperture has widened beyond passenger cars — drones, eVTOL aircraft, last-mile delivery robots, agricultural autonomy — because the underlying problem is the same everywhere. It takes a billion miles to make a safe autonomous system. It takes one bad mile to ruin it. Our job is to make sure those bad miles happen in simulation.