TL;DR

PerfectBit creates high-quality training data that's correct by construction. We verify against physics simulators, scientific databases, formal proof systems. It's what models need to cure hallucination, close common-sense gaps, and reach superintelligence. Reinforcement Learning from Verified Rewards (RLVR) was an early step in this direction. We go further. LLMs, robotics, AI for Science, and more.

https://youtu.be/RrFKfbO6XlY

The problem: the failures standing between today's models and superintelligence

Frontier labs are bottlenecked on four data problems:

• Hallucination. Models trained on noisy web scrapes produce confident, fluent statements that are wrong.
• Common-sense failure. Models that solve graduate-level math still trip on trivial problems for a five-year-old.
• Information scarcity. Scientific facts, formal derivations, peer-reviewed findings — they're a thin minority of tokens in any open-web scrape, drowned out by orders of magnitude of recipes, marketing copy, forum chatter, and AI-generated slop. Scaling the scrape doesn't change the ratio.
• Human annotation doesn't scale. It's slow, expensive, and capped at human ability — superintelligence won't come from labeling at human throughput.

What we do

• PerfectBit creates high-quality training data that's correct by construction. Every sample is generated against an oracle — physics simulator, scientific database, formal proof system — and verified before it ships, in text, code, image, audio, or video. For LLMs, we project observations about the world into natural language; for verticals like robotics and AI-for-science, we ship purpose-built datasets.

Why us

We've trained the models we're now building data for.

• Peter Vajda — Former Director of Media Generation at Meta. Eleven years at Meta. Most recently led Media GenAI foundation-model R&D: Emu (text-to-image), image editing, Movie Gen, text-to-video, video editing, character-consistent generation. Earlier led efficient deep learning for computer vision powering on-device Augmented Reality and Virtual Reality (AR/VR) models. Assistant Visiting Professor at Stanford before Meta. PhD in Computer Science.
• Seiji Yamamoto — Led teams in the Core Llama group at Meta Superintelligence Labs. Nine years at Meta as a Senior Staff Research Scientist across LLM pre- and post-training, inference optimization, full-duplex speech, and computer vision. PhD in Physics, publications in Proceedings of the National Academy of Sciences and Physical Review Letters (co-authored with a Fields Medalist). Stanford, Rice, Columbia; postdoc at a National Lab.

Between us: foundation-model training across text, image, video, and speech, shipped to billions of users — plus scientific chops to design the verifier stacks.

We’d love to talk more

We're talking to a small number of frontier AI labs about pilot engagements.

• Heads of data / data partnerships at frontier labs
• Research leads running pre-training, mid-training, post-training, or reasoning programs and shopping for a corpus that doesn't exist yet
• Model-training teams preparing a next-gen training run who want a correct-by-construction supplement in the mix
• Researchers in formal methods, scientific computing, or domain-specific simulators who want to contribute a verifier stack — we'd love to talk

We're also hiring research scientists and engineers in San Francisco (in-person). If you've shipped foundation-model training, built formal/simulation tooling or spent time developing AI training data at production scale, please reach out.

Contact

• Web: https://perfectbit.ai
• Email: founders@perfectbit.ai
• Founders: Peter Vajda, Seiji Yamamoto