PerfectBit

Training data for frontier AI labs

Spring 2026ActiveB2BInfrastructureAISan Francisco, CA, USA

Company

We create a new kind of data for training AI models. Most LLMs are pre-trained on noisy web-scraped text, but they hallucinate and still fail on tasks that humans find trivial. PerfectBit creates high-quality training data that's correct by construction. We verify against physics simulators, scientific databases, formal proof systems. LLMs, robotics, AI for Science, and more.

Note: This is a preliminary assessment based on limited publicly available information. We did not have access to LinkedIn profiles or live product screenshots for this analysis. We will update this entry with a more thorough review soon.

Verdict

High Signal

Market Opportunity

Frontier AI labs (OpenAI, Anthropic, Google DeepMind, Meta, xAI) collectively spend billions on training data and are actively seeking higher-quality, verifiable data to reduce hallucinations. Robotics and AI-for-Science are rapidly growing verticals with acute data quality problems. The ICP is narrow and clear: labs and companies doing serious model training. Monetization is straightforward — data licensing or bespoke data contracts at high price points.

High Signal

Founder Signal

Exceptionally strong team. Peter Vajda spent 11 years at Meta as Director of Media Generation, directly leading foundation model R&D including Emu, Movie Gen, and on-device AR/VR models, plus an Assistant Professor role at Stanford. Seiji Yamamoto spent 9 years at Meta as Senior Staff Research Scientist at Meta Superintelligence Labs covering LLM pre-training, post-training, inference optimization, and holds a Physics PhD with publications in PNAS and Physical Review Letters — directly relevant to physics-simulator-verified data. This is one of the strongest technical pairings imaginable for this problem.

Medium Signal

Competition

Scale AI and Appen dominate human-labeled data at scale. Synthetic data players like Gretel.ai, Mostly AI, and various post-training data shops (e.g., Surge AI, Labelbox) exist, but none specifically focus on correctness-by-construction via physics simulators and formal proof systems. The verification-grounded approach is meaningfully differentiated, though big labs like Meta and Anthropic are increasingly building internal synthetic data pipelines — which is a real threat.

Low Signal

Product

No press coverage, no named customers, no pricing page, no demo visible. The concept is technically credible — verification against physics simulators and formal proof systems — but there's zero evidence of a live product, paying customers, or even a waitlist. Pure description stage.

OverallB Tier

The founders are genuinely world-class — two senior Meta AI veterans with directly relevant experience in LLM pre-training and media generation, backed by serious physics credentials that make the 'correct by construction' thesis credible. The market is real and well-timed, as frontier labs are actively paying for better training data. The problem holding this back from A/S is complete absence of product evidence — no customers, no press, no demo, no revenue signal whatsoever. The other material risk is that the biggest potential customers (Meta, Google, OpenAI) are increasingly building these capabilities internally, which could shrink the addressable market fast. Watch for early customer logos — one named frontier lab contract would change this rating immediately.

Active Founders

Peter Vajda

Founder

I worked as Director of Media Generation at Meta before 2026 for 11 years. I was managing the Media GenAI foundation model research and development, including efficient media generation, text to image generation (Emu), image editing, Movie gen, text to video, video editing and character consistent image and video generation. Previously, led efficient deep learning for computer vision teams supporting on-device models for AR/VR. I was Assistant Professor at Stanford University

Seiji Yamamoto

Founder

Led teams in the Core Llama group at Meta Superintelligence Labs. Senior Staff Research Scientist across 9 years at Meta spanning LLM pre-training and post-training, inference optimization, full-duplex speech models, and computer vision vision models. Before tech: PhD in Physics, published in Proceedings of the National Academy of Sciences and Physical Review Letters, co-authored with Fields Medalist. Educated at Stanford, Rice, Columbia, post-doc at a National Lab.

PerfectBit

TierB Tier

BatchSpring 2026

Team Size2

StatusActive

LocationSan Francisco, CA, USA

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