Smart Mobility Through LiDAR

198,000+ LIDAR frames annotated in six months at 98.5% accuracy. A US-based autonomous mobility company developing AI systems for self-driving navigation and real-time object detection required high-precision LIDAR annotation support to accelerate model training and deployment. Existing annotation workflows struggled with inconsistent quality, limited 3D expertise and rising costs across complex point-cloud datasets.

High-precision LIDAR annotation accelerated autonomous driving AI training, reduced processing costs and improved scalability across complex 3D datasets.

Industry: Automotive
Service: Generative AI, Product Engineering

3.5M+: Images annotated in <5 months
98%+: Accuracy maintained
−58%: Cost vs previous annotation model

Challenge

The client needed to process large volumes of complex 3D point-cloud data under aggressive delivery timelines while maintaining strict annotation accuracy. Freelancer-led workflows produced inconsistent outputs, internal teams lacked specialized LIDAR expertise and high per-frame annotation costs limited scalability. Data security requirements also demanded a tightly controlled operational environment.

Approach

SBL Infotech deployed a dedicated workforce of 75 trained LIDAR annotation specialists focused on point-cloud segmentation, cuboid object labeling and road and lane detection workflows. Working directly within the client’s secure infrastructure, the team executed high-precision annotations optimized for autonomous driving model training. A three-level quality control framework ensured consistency and maintained 98.5% accuracy across all datasets. SLA-driven delivery processes, continuous validation cycles and close coordination with the client’s AI and engineering teams enabled faster throughput while preserving annotation integrity across complex 3D environments.

Outcome

The client replaced fragmented annotation operations with a scalable LIDAR processing pipeline capable of supporting rapid AI model development across expanding autonomous mobility datasets. Faster delivery cycles improved training velocity and reduced dependency on unreliable freelance resources while significantly lowering annotation costs. The engagement established a long-term operational framework for secure, high-volume 3D data annotation — enabling the client to scale future autonomous driving initiatives with greater speed, consistency and operational control.

IX Case studies

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