Augury vs Landing AI: Revenue, Efficiency & Valuation
Compare Augury and Landing AI on ARR per employee, revenue, headcount, valuation, funding, category rank, and source evidence.
Metric differences
Augury produces $249K ARR per employee versus $243K for Landing AI, a directional gap of $6K per employee.
| Company | ARR/employee | ARR | Employees | Valuation | Funding |
|---|---|---|---|---|---|
| Augury | $249K | $80M | 321 | $1B | $75M Series F (Feb 2025) |
| Landing AI | $243K | $25M | 103 | $0.5B | $57M Series A (Nov 2021) |
Company summaries
Augury
Augury is an AI-powered predictive maintenance platform for manufacturing and industrial equipment that uses vibration, temperature, and magnetic sensors to detect machine failures before they happen. The platform serves plant managers and maintenance teams at large manufacturers, food and beverage companies, and data centers that cannot afford unplanned downtime on critical equipment. Augury's AI models analyze continuous sensor data streams to identify early signs of mechanical degradation, predicting failures days or weeks in advance and recommending specific maintenance actions. The company has built one of the largest datasets of industrial machine health data, giving its models a compounding accuracy advantage as more equipment is connected to the platform.
Landing AI
Landing AI is a visual inspection platform for manufacturing, founded by Andrew Ng, that enables factories to build and deploy computer vision models with small datasets using a data-centric AI approach. The platform serves manufacturers across automotive, electronics, food, and pharmaceutical industries that need to automate quality inspection but lack the large labeled datasets typically required to train accurate vision models. Landing AI's Visual Prompting technology allows manufacturing engineers to create inspection models by simply highlighting defects on a few sample images, dramatically lowering the barrier to deploying computer vision on the factory floor. The company's approach reflects Andrew Ng's data-centric AI philosophy, focusing on improving data quality rather than model complexity to achieve production-grade accuracy.