platoseed
โ† All companies
PerfectBit logo

PerfectBit

Active

Training data for frontier AI labs

Spring 2026Founded 20262 peopleSan Francisco, CA, USA
Generate ideas โ†’
AI insightcan contain mistakes
Verified Training DataAPI/InfraFrontier AI labs, LLM trainingMedium competition
Moat
Physics simulators, scientific databases, formal proof verification; provably correct data.
Key risk
Frontier labs may build in-house data pipelines; limited to niche high-quality datasets.
Why now
LLMs need better training data; hallucination problem drives demand for verified data sources.
Competitors
Scale AI, Labelbox, AWS data pipelines, in-house frontier lab solutions
โ†ป Pivot / rename signal

This company has previously operated under โ€œGikl, Incโ€. A rename frequently marks a pivot in positioning or product โ€” useful raw material for variant ideas.

About

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.

Founders ยท 2

Peter Vajda
Peter VajdaFounder
MetaStanford๐ŸŽ“ Stanford University

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
Seiji YamamotoFounder
MetaStanfordColumbia

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.

B2BInfrastructureAI

Related startups

Also in Spring 2026