Encord, the data infrastructure company for multimodal AI, announced the launch of its Physical AI suite. With support for 3D, LiDAR, and point cloud data, the platform streamlines the entire AI lifecycle for robotics, autonomous vehicles, and drone development teams, from raw sensor data management to model debugging.
Developing AI for physical systems involves navigating complex data types and fragmented workflows. Encord’s platform addresses these challenges by integrating critical capabilities into a single, cohesive environment. This enables development teams to accelerate the delivery of advanced autonomous capabilities with higher quality data and deeper insights, while improving operational efficiency and reducing costs.
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Key capabilities:
- Scalable and Secure Data Ingestion: Teams can securely synchronize data from their cloud buckets directly into Encord. The platform seamlessly ingests and intelligently manages high-volume, continuous raw sensor data streams, including LiDAR point clouds, camera imagery, and diverse telemetry, as well as commonly supported industry file formats (such as MCAP).
- Data Curation and Quality Control: The platform delivers automated tools for data quality checks and intelligent curation, helping teams identify critical edge cases and structure data for optimal model training. Teams can efficiently filter, batch, and select precise data segments for specific annotation and training needs.
- AI-Assisted Data Labeling: The platform supports AI-assisted labeling capabilities, including automated object tracking and single-shot labeling across scenes. It supports a wide array of annotation types and ensures high-precision labels across different sensor modalities and over time, even as annotation requirements evolve.
- AI Model Evaluation and Debugging: The platform provides tools to evaluate model predictions against ground truth, pinpointing failure modes and identifying the exact data that led to unexpected outcomes. This capability shortens iteration cycles, allowing teams to quickly diagnose issues, refine models, and improve AI accuracy for fail-safe applications.
- Workflow Management and Collaboration: Built for large-scale operations, the platform includes robust workflow management tools. Administrators can distribute tasks among annotators, track performance, assign QA reviews, and ensure compliance across projects.
Source: Businesswire