Safran’s parts inspectors used to spend 20 to 30 minutes checking a newly made toilet lid for flaws like paint bubbles, scratches, and uneven surfaces. Now, with Loopr AI, that inspection takes 5 to 10 minutes, and the system automatically creates a defect and inspection sheet before the part moves to the next stage, according to Safran’s senior director and head of digital production, robotics, and automation, Bindioa Ouali.
The French aviation manufacturer is using artificial intelligence to streamline quality assurance, but the bigger lesson is about data. Zheng Yao, a principal research scientist at Lehigh University’s Energy Research Center, said AI works well only if companies have enough data to train it. When they do not, synthetic data can fill the gap by creating artificial information that mimics real data points.
That is where Loopr AI comes in. Priyansha Bagaria, Loopr’s founder and CEO, said the company generates new training samples from existing images and alters them to expand the number of examples algorithms can learn from. Safran has been running pilot projects with Loopr for two years to see whether AI could improve defect identification while reducing inspection and documentation time.
Bagaria said the software is effective at detecting cosmetic defects in cabin assemblies by comparing them against production drawings and the listed parts to use. She said AI helps remove subjectivity, retain operator knowledge, automate repetitive tasks, and standardize processes.
The system also uses data tied to specific manufacturing components and lighting conditions that mimic where the parts will eventually be installed. On Safran’s paint inspection program, a robotic arm photographed parts before and after paint application, then sent the images to Loopr for analysis. In a hybrid setup for cabin assemblies that include a lavatory or galley, inspectors manually take photos at different angles and upload them to Safran’s computer system.
Ouali said the project delivered a 90.9% recall, meaning it could identify defects such as scratches, paint discoloration, and paint bubbles. He also said the faster inspection process has allowed the company to produce 10% to 15% more than before because throughput increased enough to send more parts to final assembly.
Founders building in regulated industries should read this as a playbook, not a headline. The technology matters, but the data layer is the real bottleneck. If your models cannot learn from enough real-world examples, synthetic data may be the difference between a slow manual workflow and a faster system that scales.
The takeaway is simple: AI does not replace discipline. It rewards it. Companies that document well, train carefully, and keep humans in the loop can turn inspection from a cost center into a throughput advantage.
