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Industrial data layer for robotics training
We 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.
Vision Lab positions itself as an industrial data layer for robotics, offering egocentric footage and SOP-level process knowledge to train and evaluate robotics systems across multiple industries. They claim a global factory capture network and specialized vision models to enable structured industrial data and high-efficiency annotation.
The product provides: (1) diverse, SOP-structured industrial production data derived from egocentric footage across 50+ industries; (2) fine-tuned vision models with temporal understanding for industrial environments and efficient annotation; (3) a global factory capture network with standardized protocols, verified ground truth, and data rights management. The workflow appears to convert raw industrial video into structured knowledge, with training within industry (TWI) SOPs and step-by-step task breakdowns, plus an API (Gemini) that outputs annotated sequences and example outputs.
Who it’s for: Industrial enterprises and research teams needing structured industrial data and robotics training data across manufacturing, biotech, electronics, automotive, pharma, and related sectors; organizations building foundation models or deploying industrial AI.
Backed by Y Combinator; mentions of a broad factory network, live demo outputs, and API access imply early traction and investor interest.
James is the co-founder and CEO at LineWise. MBA from MIT. Former McKinsey Ops consultant.
Zhichu is the co-founder and CTO at LineWise. MIT PhD. Built robotics automation for materials research.
Will is the co-founder and CPO at Vision Lab. MSE in AI & Robotics at UPenn GRASP Lab. Former founding engineer at Greyscale AI and Bestmow.
AI that troubleshoots your production line
LineWise builds an AI assistant for manufacturing teams to quickly diagnose and fix production-line faults by integrating siloed data from IoT sensors, maintenance logs, and manuals; it ranks likely causes, guides technicians through fixes, and records resolutions to enable faster troubleshooting and preventative actions.
Formerly “Comply Genie”, “LineWise” · why startups rename →
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