Praxis AI
Praxis captures the data physical AI is bottlenecked on: egocentric video, 3D scans, and multimodal capture from inside real industrial and residential environments. Embedded within publicly listed and unicorn-scale conglomerates, we reach 60k workers across 5 continents and 150+ environment types, supplying the real-world training data frontier labs and humanoid companies can't source anywhere else.
Y Combinator
yc-company-directory #3109Praxis captures the data physical AI is bottlenecked on: egocentric video, 3D scans, and multimodal capture from inside real industrial and residential environments. Embedded within publicly listed and unicorn-scale conglomerates, we reach 60k workers across 5 continents and 150+ environment types, supplying the real-world training data frontier labs and humanoid companies can't source anywhere else.
sourcePraxis AI is classified as AI infrastructure from source descriptions and fund-directory context. Similar companies below are ranked from offline product facets and meaningful description overlap; category and source affiliation can only strengthen an existing product match. For lookup-created rows, the profile starts as pending enrichment until deeper source collection runs.
Local clusters rerank extracted product concepts and description terms. The generated graph remains the fallback for sparse descriptions: offline reciprocal product-similarity graph: extracted concepts + description terms + bounded category/affiliation boosts. Minimum fallback score: 0; max competitors: 6.
Companies similar to Praxis AI
A deterministic local cluster built from offline product facets and meaningful description terms. Category and selected-institution affiliation provide bounded tie-breaks. Open any peer to continue exploring its cluster.
Vision Lab
match 83We capture and structure real factory workflows at scale by combining first-person industrial video with SOP-level process knowledge. This enables robotics and AI labs to train on real production data, not just controlled lab environments.
shared extracted concept terms: video; shared product terms: capture, video, real, industrial; shared product theme: context and memory; same category: AI infrastructure; shared affiliation: Y Combinator
BioStack Platforms
match 60BioStack is building the data engine for healthcare and drug discovery AI. The bottleneck is not models. It is access to high-quality biological data. Clinical and experimental data is fragmented, unstructured, and locked inside hospitals, labs, and CROs, while generating new data is slow and expensive. BioStack fixes this with proprietary clinical and preclinical data pipelines that turn real biomedical workflows into ML-ready training environments. We structure longitudinal multimodal data acr
shared product terms: bottleneck, inside, labs, real; same category: AI infrastructure; shared affiliation: Y Combinator
HyBird
match 57Making physical asset inspection easy, by improving data capture, and processing, analysing and visualising it in an easy-to-use digital 3D environment.
shared extracted concept terms: capture, 3d; shared product terms: physical, capture, 3d, environment; shared product theme: context and memory
Asimov
match 54Asimov collects real-world human movement data from households and businesses to train humanoid robots. Unlike factory datasets that capture the same tasks in the same environments, we deliver the full diversity of real human environments, thousands of hours a day to leading labs.
shared product terms: real, train, humanoid, capture; shared product theme: context and memory; shared affiliation: Y Combinator
Whitebalance
match 50Uses use AI, computer vision, and 3D graphics to help companies build narrative, capture, edit, and manage video in a fully online and decentralized way.
shared extracted concept terms: 3d; shared product terms: capture, video, 3d, companie; shared product theme: context and memory; same category: AI infrastructure
Human Archive
match 49We’re archiving the physical world for embodied intelligence by collecting and labeling aligned multimodal data. To build dexterous and perceptive robots that generalize robustly, we need massive amounts of real-world data across multiple modalities and environments. We have thought deeply about the fine line between biomimicry and its application to humanoid systems. Based on this research, we design and deploy custom hardware across residential and manufacturing settings. We then post-process
shared product terms: physical, multimodal, real, environment; shared affiliation: Y Combinator