FIG 01 builds physical AI solutions that empower robots to handle complex industrial automation tasks, combining advanced perception and adaptive control to operate in variable manufacturing environments. Their platform lets manufacturers deploy flexible automation without extensive reprogramming, streamlining production lines and reducing downtime.
Funding
Funding not disclosed
Founders
Product
Problem
Manufacturers often rely on separate software analytics and manual interventions to monitor equipment health, leading to delayed detection of faults, unplanned downtime, and suboptimal production efficiency.
Solution
FIG 01 provides physical AI modules that embed machine‑learning models directly into industrial hardware, enabling real‑time monitoring, predictive maintenance, and autonomous process optimization on the factory floor. The system continuously collects sensor data, runs inference locally to detect anomalies, and adjusts control parameters without human oversight. By integrating AI at the edge, the platform reduces latency, eliminates the need for extensive cloud infrastructure, and delivers actionable insights that keep equipment running at peak performance. Operators can view live diagnostics through a simple interface, while the AI continuously learns from operational data to improve accuracy over time.
Target Audience
Primary customers are mid‑to‑large scale manufacturers and industrial automation engineers seeking to minimize downtime and increase production efficiency through autonomous, data‑driven control.
Features
- Edge‑deployed AI hardware that runs inference on‑device for instant fault detection
- Predictive maintenance algorithms that forecast equipment failures before they occur
- Closed‑loop control loops that automatically adjust machine settings to optimize throughput
- Real‑time data aggregation from existing factory sensors with minimal integration effort
- On‑device model updates that incorporate new operational data without downtime
- Dashboard for operators showing health scores, alerts, and recommended actions